The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang...The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.展开更多
Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-netwo...Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.展开更多
This study investigated the impact of the xu-argument-based continuation on Chinese high school students’English syntactic complexity captured using verb-argument constructions(VACs)over an 8-week period.Participants...This study investigated the impact of the xu-argument-based continuation on Chinese high school students’English syntactic complexity captured using verb-argument constructions(VACs)over an 8-week period.Participants were two comparable groups of students:one group worked with English input texts(i.e.,E-E),while the other worked with Chinese input texts with the same content(i.e.,C-E).The results showed that over time,the E-E group exhibited a greater tendency to use a wider range of VACs,such as caused-motion constructions,attributives,passives,and phrasal verbs.At the same time,they reduced their use of simpler VACs like intransitive-motion and simple transitive constructions,especially when compared to the C-E group.This pattern was also evident in the topic-based writing during the posttest.These findings strongly support the effectiveness of xu-argument-based continuation tasks in promoting the development of L2 VAC knowledge.They suggest that tasks combining language input with output can significantly enhance learners’ability to use more sophisticated VACs.展开更多
Text,as a fundamental carrier of human language and culture,exhibits high structural and semantic complexity.Its systematic analysis is essential for understanding linguistic patterns and cultural transmission.A Dream...Text,as a fundamental carrier of human language and culture,exhibits high structural and semantic complexity.Its systematic analysis is essential for understanding linguistic patterns and cultural transmission.A Dream of Red Mansions and All Men Are Brothers,two masterpieces of Chinese classical literature,have long been central to debates regarding the authorship of their later chapters.Previous studies,often based on word-frequency statistics,function word distributions,entropy measures,and complex network analyses,have provided valuable insights into stylistic differences;however,they remain limited in capturing cross-scale structural features.To address this gap,we apply a multi-scale structural complexity approach based on character-frequency time series to analyze the structural evolution of both novels under various segmentation strategies.Our results reveal significant differences in peak complexity positions,overall complexity levels,and intra-textual variations between the two works,which are closely linked to changes in authorship and stylistic patterns.This study not only provides new quantitative evidence for resolving authorship disputes in classical literature but also demonstrates,from the perspective of structural complexity,the profound depth and unique charm of Chinese literary expression,highlighting the richness of Chinese language and culture.Moreover,it emphasizes the potential of structural complexity analysis as a versatile tool for textual analysis and style attribution.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
Numerous studies have examined the impact ofwater quality degradation on bacterial community structure,yet insights into its effects on the bacterial ecological networks remain scarce.In this study,we investigated the...Numerous studies have examined the impact ofwater quality degradation on bacterial community structure,yet insights into its effects on the bacterial ecological networks remain scarce.In this study,we investigated the diversity,composition,assembly patterns,ecological networks,and environmental determinants of bacterial communities across 20 ponds to understand the impact of water quality degradation.Our findings revealed that water quality degradation significantly reduces the α-diversity of bacterial communities in water samples,while sediment samples remain unaffected.Additionally,water quality deterioration increases the complexity of bacterial networks in water samples but reduces it in sediment samples.These shifts in bacterial communities were primarily governed by deterministic processes,with heterogeneous selection being particularly influential.Through redundancy analysis(RDA),multiple regression on matrices(MRM),and Mantel tests,we identified dissolved oxygen(DO),ammonium nitrogen(NH_(4)^(+)-N),and C/N ratio as key factors affecting the composition and network complexity of bacterial communities in both water and sediment.Overall,this study contributes a novel perspective on the effect ofwater quality deterioration on microbial ecosystems and provides valuable insights for improving ecological evaluations and biomonitoring practices related to water quality management.展开更多
Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions ...Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.展开更多
Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning fr...Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus...The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus(SCN)integrates environmental signals to maintain complex and robust circadian rhythms.Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function.Synchrony involves coordinated neuronal firing for robust rhythms,while complexity reflects diverse activity patterns and interactions,indicating adaptability.Interestingly,the SCN retains circadian rhythms in vitro,demonstrating intrinsic rhythmicity.This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels,based on Bagrov et al.’s approach.By examining structural complexity and local complexities across scales,we aim to understand how tetrodotoxin,a neurotoxin that inhibits action potentials,affects SCN neurons.Our method captures critical scales in neuronal interactions that traditional methods may overlook.Validation with the Goodwin model confirms the reliability of our observations.By integrating experimental data with theoretical models,this study provides new insights into the effects of tetrodotoxin(TTX)on neuronal complexities,contributing to the understanding of circadian rhythms.展开更多
The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertai...The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertainties. This study identifies 20 complexity factors through expert interviews and literature, categorising them into six groups. The Analytical Hierarchy Process evaluated the significance of different factors, establishing their corresponding weights to enhance adaptive project scheduling. A system dynamics (SD) model is developed and tested to evaluate the dynamic behaviour of identified complexity factors. The model simulates the impact of complexity on total project duration (TPD), revealing significant deviations from initial deterministic estimates. Data collection and analysis for reliability tests, including normality and Cronbach alpha, to validate the model’s components and expert feedback. Sensitivity analysis confirmed a positive relationship between complexity and project duration, with higher complexity levels resulting in increased TPD. This relationship highlights the inadequacy of static planning approaches and underscores the importance of addressing complexity dynamically. The study provides a framework for enhancing planning systems through system dynamics and recommends expanding the model to ensure broader applicability in diverse construction projects.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
This study aims to validate the Object-Oriented User Interface Customization(OOUIC)framework by employing Use Case Analysis(UCA)to facilitate the development of adaptive User Interfaces(UIs).The OOUIC framework advoca...This study aims to validate the Object-Oriented User Interface Customization(OOUIC)framework by employing Use Case Analysis(UCA)to facilitate the development of adaptive User Interfaces(UIs).The OOUIC framework advocates for User-Centered Design(UCD)methodologies,including UCA,to systematically identify intricate user requirements and construct adaptive UIs tailored to diverse user needs.To operationalize this approach,thirty users of Product Lifecycle Management(PLM)systems were interviewed across six distinct use cases.Interview transcripts were subjected to deductive content analysis to classify UI objects systematically.Subsequently,adaptive UIs were developed for each use case,and their complexity was quantitatively compared against the original system UIs.The results demonstrated a significant reduction in complexity across all adaptive UIs(Mean Difference,MD=0.11,t(5)=8.26,p<0.001),confirming their superior efficiency.The findings validate the OOUIC framework,demonstrating that UCD effectively captures complex requirements for adaptive UI development,while adaptive UIs mitigate interface complexity through object reduction and optimized layout design.Furthermore,UCA and deductive content analysis serve as robust methodologies for object categorization in adaptive UI design.Beyond eliminating redundant elements and prioritizing object grouping,designers can further reduce complexity by adjusting object dimensions and window sizing.This study underscores the efficacy of UCA in developing adaptive UIs and streamlining complex interfaces.Ultimately,UCD proves instrumental in gathering intricate requirements,while adaptive UIs enhance usability by minimizing object clutter and refining spatial organization.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
In this paper,we study the problem of sampling complexity for channel discrimination with respect to two different strategies:product strategy and adaptive strategy.We first formally introduce the definitions of the s...In this paper,we study the problem of sampling complexity for channel discrimination with respect to two different strategies:product strategy and adaptive strategy.We first formally introduce the definitions of the sampling complexity of the channels under the framework of hypothesis testing,wherein the goal is to determine the minimum number of samples needed to reach a desired error probability.We then establish the lower and upper bounds on the sampling complexity of the symmetric,asymmetric,and error exponent hypothesis testing settings.We show that,by imposing product strategy on testing,the bounds are always characterized by the generalized channel divergence,while with adaptive strategy,the bounds are characterized by the amortized channel divergence.Finally,we analyze two concrete examples,and obtain that the adaptive strategy can not lead to an advantage to the problem of determining the sampling complexity for classical-quantum channels,which can bring advantages for generalized amplitude damping channels.展开更多
Binary sequences constructed by Legendre symbols are widely used in communication and cryptography since they have many good pseudo-random properties.In this paper,we determine the 2-adic complexity of the sum sequenc...Binary sequences constructed by Legendre symbols are widely used in communication and cryptography since they have many good pseudo-random properties.In this paper,we determine the 2-adic complexity of the sum sequence of any k many Legendre sequences and show that the 2-adic complexity of the sum sequences of any k many Legendre sequences reaches the maximum by proving the case of k=2 and 3,which implies that the sum sequences can resist the attack of rational approximation algorithm.展开更多
Traditionally,passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk.Consequently,the current research primarily focuses on developing control algorithms to limit the maximum acce...Traditionally,passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk.Consequently,the current research primarily focuses on developing control algorithms to limit the maximum acceleration and jerk of the vehicle in order to improve passenger comfort.However,naturalistic driving studies demonstrate that such simple characteristics are insufficient for accurately evaluating passenger comfort.This study identifies motion complexity as a key factor of passenger comfort.A series of naturalistic driving studies are conducted,during which passenger comfort is assessed using a 5-point Likert scale.Moreover,a real-time passenger comfort measurement based on electromyography(EMG)and stepwise regression is proposed to facilitate seamless data collection.Time-series features representing motion complexity are then introduced to better describe passenger comfort.Hierarchical regression confirms that simple characteristics of motion are insufficient to explain passenger comfort,and shows that the proposed motion complexity features have a substantial effect on passenger comfort.Finally,a machine learning-based real-time passenger comfort estimation method is developed according to the foregoing findings.Experimental results show that the proposed method can accurately estimate passenger comfort in real-time using only vehicle motion information.The findings of this study suggest that vehicle motion complexity should be considered in future passenger comfort studies.展开更多
This study examined how psychological meaningfulness moderates job complexity and work-family conflict in Nigerian secondary school teachers.This study included 1694 teachers from 17 Nigerian secondary schools(female=6...This study examined how psychological meaningfulness moderates job complexity and work-family conflict in Nigerian secondary school teachers.This study included 1694 teachers from 17 Nigerian secondary schools(female=69.54%,mean age=33.19,SD=6.44 years).The participants completed the Work-family Conflict Scale,Job Complexity Scale,and Psychological Meaningfulness Scale.Study design was cross-sectional.Hayes PROCESS macro analysis results indicate a higher work-family conflict with job complexity among the secondary school teachers.While psychological meaningfulness was not associated with work-family conflict,it moderated the link between job complexity and work-family conflict in secondary school teachers such that a meaningful work endorsement is associated with lower employee’s work-life conflict.Thesefindings point to the importance of job functions to quality of family life.The studyfindings also suggest a need for supporting psychological meaningfulness for healthy work related quality of family life based on balancing work and family role demands.展开更多
Three copper(Ⅱ),nickel and cadmium(Ⅱ)complexes,namely[Cu_(2)(μ-H2dbda)2(phen)2]·2H_(2)O(1),[Ni(μ-H2dbda)(μ-bpb)(H_(2)O)2]n(2),and[Cd(μ-H2dbda)(μ-bpa)]n(3),have been constructed hydrothermally using H4dbda(...Three copper(Ⅱ),nickel and cadmium(Ⅱ)complexes,namely[Cu_(2)(μ-H2dbda)2(phen)2]·2H_(2)O(1),[Ni(μ-H2dbda)(μ-bpb)(H_(2)O)2]n(2),and[Cd(μ-H2dbda)(μ-bpa)]n(3),have been constructed hydrothermally using H4dbda(4,4'-dihydroxy-[1,1'-biphenyl]-3,3'-dicarboxylic acid),phen(1,10-phenanthroline),bpb(1,4-bis(pyrid-4-yl)benzene),bpa(bis(4-pyridyl)amine),and copper,nickel and cadmium chlorides at 160℃.The products were isolated as stable crystalline solids and were characterized by IR spectra,elemental analyses,thermogravimetric analyses,and singlecrystal X-ray diffraction analyses.Single-crystal X-ray diffraction analyses revealed that three complexes crystallize in the monoclinic P21/n,tetragonal I42d,and orthorhombic P21212 space groups.The complexes exhibit molecular dimers(1)or 2D metal-organic networks(2 and 3).The catalytic performances in the Knoevenagel reaction of these complexes were investigated.Complex 1 exhibits an effective catalytic activity and excellent reusability as a heterogeneous catalyst in the Knoevenagel reaction at room temperature.CCDC:2463800,1;2463801,2;2463802,3.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.U2244225 and 42020104005)the Ministry of Education of China(111 Project)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)and China Geological Survey(No.DD20211391)。
文摘The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.
文摘Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.
文摘This study investigated the impact of the xu-argument-based continuation on Chinese high school students’English syntactic complexity captured using verb-argument constructions(VACs)over an 8-week period.Participants were two comparable groups of students:one group worked with English input texts(i.e.,E-E),while the other worked with Chinese input texts with the same content(i.e.,C-E).The results showed that over time,the E-E group exhibited a greater tendency to use a wider range of VACs,such as caused-motion constructions,attributives,passives,and phrasal verbs.At the same time,they reduced their use of simpler VACs like intransitive-motion and simple transitive constructions,especially when compared to the C-E group.This pattern was also evident in the topic-based writing during the posttest.These findings strongly support the effectiveness of xu-argument-based continuation tasks in promoting the development of L2 VAC knowledge.They suggest that tasks combining language input with output can significantly enhance learners’ability to use more sophisticated VACs.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179,11875042,and12150410309)the Natural Science Foundation of Shanghai(Grant No.21ZR1443900)。
文摘Text,as a fundamental carrier of human language and culture,exhibits high structural and semantic complexity.Its systematic analysis is essential for understanding linguistic patterns and cultural transmission.A Dream of Red Mansions and All Men Are Brothers,two masterpieces of Chinese classical literature,have long been central to debates regarding the authorship of their later chapters.Previous studies,often based on word-frequency statistics,function word distributions,entropy measures,and complex network analyses,have provided valuable insights into stylistic differences;however,they remain limited in capturing cross-scale structural features.To address this gap,we apply a multi-scale structural complexity approach based on character-frequency time series to analyze the structural evolution of both novels under various segmentation strategies.Our results reveal significant differences in peak complexity positions,overall complexity levels,and intra-textual variations between the two works,which are closely linked to changes in authorship and stylistic patterns.This study not only provides new quantitative evidence for resolving authorship disputes in classical literature but also demonstrates,from the perspective of structural complexity,the profound depth and unique charm of Chinese literary expression,highlighting the richness of Chinese language and culture.Moreover,it emphasizes the potential of structural complexity analysis as a versatile tool for textual analysis and style attribution.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金supported by Zhejiang Provincial Natural Science Foundation of China(No.LTGS24D010004)the National Natural Science Foundation of China grant(No.42307064)+2 种基金the National Students’platform for innovation and entrepreneurship training program(No.202410346054)Hangzhou“Young science and technology talent cultivation”project(No.4305F45623004)the Fundamental Research Funds for Climbing Project from Hangzhou Normal University(No.KYQD-2023-217).
文摘Numerous studies have examined the impact ofwater quality degradation on bacterial community structure,yet insights into its effects on the bacterial ecological networks remain scarce.In this study,we investigated the diversity,composition,assembly patterns,ecological networks,and environmental determinants of bacterial communities across 20 ponds to understand the impact of water quality degradation.Our findings revealed that water quality degradation significantly reduces the α-diversity of bacterial communities in water samples,while sediment samples remain unaffected.Additionally,water quality deterioration increases the complexity of bacterial networks in water samples but reduces it in sediment samples.These shifts in bacterial communities were primarily governed by deterministic processes,with heterogeneous selection being particularly influential.Through redundancy analysis(RDA),multiple regression on matrices(MRM),and Mantel tests,we identified dissolved oxygen(DO),ammonium nitrogen(NH_(4)^(+)-N),and C/N ratio as key factors affecting the composition and network complexity of bacterial communities in both water and sediment.Overall,this study contributes a novel perspective on the effect ofwater quality deterioration on microbial ecosystems and provides valuable insights for improving ecological evaluations and biomonitoring practices related to water quality management.
基金supported by the Fundamental Research Funds of Chinese Academy of Forestry(Nos.CAFYBB2022SY037,CAFYBB2021ZA002 and CAFYBB2022QC002)the Basic Research Foundation of Yunnan Province(Grant No.202201AT070264).
文摘Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.
基金supported by the National Natural Science Foundation of China(32370703)the CAMS Innovation Fund for Medical Sciences(CIFMS)(2022-I2M-1-021,2021-I2M-1-061)the Major Project of Guangzhou National Labora-tory(GZNL2024A01015).
文摘Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179,11875042,and 12150410309)the Natural Science Foundation of Shanghai(Grant No.21ZR1443900).
文摘The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus(SCN)integrates environmental signals to maintain complex and robust circadian rhythms.Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function.Synchrony involves coordinated neuronal firing for robust rhythms,while complexity reflects diverse activity patterns and interactions,indicating adaptability.Interestingly,the SCN retains circadian rhythms in vitro,demonstrating intrinsic rhythmicity.This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels,based on Bagrov et al.’s approach.By examining structural complexity and local complexities across scales,we aim to understand how tetrodotoxin,a neurotoxin that inhibits action potentials,affects SCN neurons.Our method captures critical scales in neuronal interactions that traditional methods may overlook.Validation with the Goodwin model confirms the reliability of our observations.By integrating experimental data with theoretical models,this study provides new insights into the effects of tetrodotoxin(TTX)on neuronal complexities,contributing to the understanding of circadian rhythms.
文摘The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertainties. This study identifies 20 complexity factors through expert interviews and literature, categorising them into six groups. The Analytical Hierarchy Process evaluated the significance of different factors, establishing their corresponding weights to enhance adaptive project scheduling. A system dynamics (SD) model is developed and tested to evaluate the dynamic behaviour of identified complexity factors. The model simulates the impact of complexity on total project duration (TPD), revealing significant deviations from initial deterministic estimates. Data collection and analysis for reliability tests, including normality and Cronbach alpha, to validate the model’s components and expert feedback. Sensitivity analysis confirmed a positive relationship between complexity and project duration, with higher complexity levels resulting in increased TPD. This relationship highlights the inadequacy of static planning approaches and underscores the importance of addressing complexity dynamically. The study provides a framework for enhancing planning systems through system dynamics and recommends expanding the model to ensure broader applicability in diverse construction projects.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the National Natural Science Foundation of China(Grant No.72301061).
文摘This study aims to validate the Object-Oriented User Interface Customization(OOUIC)framework by employing Use Case Analysis(UCA)to facilitate the development of adaptive User Interfaces(UIs).The OOUIC framework advocates for User-Centered Design(UCD)methodologies,including UCA,to systematically identify intricate user requirements and construct adaptive UIs tailored to diverse user needs.To operationalize this approach,thirty users of Product Lifecycle Management(PLM)systems were interviewed across six distinct use cases.Interview transcripts were subjected to deductive content analysis to classify UI objects systematically.Subsequently,adaptive UIs were developed for each use case,and their complexity was quantitatively compared against the original system UIs.The results demonstrated a significant reduction in complexity across all adaptive UIs(Mean Difference,MD=0.11,t(5)=8.26,p<0.001),confirming their superior efficiency.The findings validate the OOUIC framework,demonstrating that UCD effectively captures complex requirements for adaptive UI development,while adaptive UIs mitigate interface complexity through object reduction and optimized layout design.Furthermore,UCA and deductive content analysis serve as robust methodologies for object categorization in adaptive UI design.Beyond eliminating redundant elements and prioritizing object grouping,designers can further reduce complexity by adjusting object dimensions and window sizing.This study underscores the efficacy of UCA in developing adaptive UIs and streamlining complex interfaces.Ultimately,UCD proves instrumental in gathering intricate requirements,while adaptive UIs enhance usability by minimizing object clutter and refining spatial organization.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金supported by the Fundamental Research Funds for the Central Universities(Grant Nos.24CX03003A,23CX03011A,22CX03005A)the Natural Science Foundation of Shandong Province(Grant Nos.ZR2023MA025,ZR2021LL2002)from the NSFC(Grant Nos.11775306,11571220).
文摘In this paper,we study the problem of sampling complexity for channel discrimination with respect to two different strategies:product strategy and adaptive strategy.We first formally introduce the definitions of the sampling complexity of the channels under the framework of hypothesis testing,wherein the goal is to determine the minimum number of samples needed to reach a desired error probability.We then establish the lower and upper bounds on the sampling complexity of the symmetric,asymmetric,and error exponent hypothesis testing settings.We show that,by imposing product strategy on testing,the bounds are always characterized by the generalized channel divergence,while with adaptive strategy,the bounds are characterized by the amortized channel divergence.Finally,we analyze two concrete examples,and obtain that the adaptive strategy can not lead to an advantage to the problem of determining the sampling complexity for classical-quantum channels,which can bring advantages for generalized amplitude damping channels.
文摘Binary sequences constructed by Legendre symbols are widely used in communication and cryptography since they have many good pseudo-random properties.In this paper,we determine the 2-adic complexity of the sum sequence of any k many Legendre sequences and show that the 2-adic complexity of the sum sequences of any k many Legendre sequences reaches the maximum by proving the case of k=2 and 3,which implies that the sum sequences can resist the attack of rational approximation algorithm.
基金Supported by National Natural Science Foundation of China(Grant No.52221005)Tsinghua-Toyota Joint Research Fund.
文摘Traditionally,passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk.Consequently,the current research primarily focuses on developing control algorithms to limit the maximum acceleration and jerk of the vehicle in order to improve passenger comfort.However,naturalistic driving studies demonstrate that such simple characteristics are insufficient for accurately evaluating passenger comfort.This study identifies motion complexity as a key factor of passenger comfort.A series of naturalistic driving studies are conducted,during which passenger comfort is assessed using a 5-point Likert scale.Moreover,a real-time passenger comfort measurement based on electromyography(EMG)and stepwise regression is proposed to facilitate seamless data collection.Time-series features representing motion complexity are then introduced to better describe passenger comfort.Hierarchical regression confirms that simple characteristics of motion are insufficient to explain passenger comfort,and shows that the proposed motion complexity features have a substantial effect on passenger comfort.Finally,a machine learning-based real-time passenger comfort estimation method is developed according to the foregoing findings.Experimental results show that the proposed method can accurately estimate passenger comfort in real-time using only vehicle motion information.The findings of this study suggest that vehicle motion complexity should be considered in future passenger comfort studies.
文摘This study examined how psychological meaningfulness moderates job complexity and work-family conflict in Nigerian secondary school teachers.This study included 1694 teachers from 17 Nigerian secondary schools(female=69.54%,mean age=33.19,SD=6.44 years).The participants completed the Work-family Conflict Scale,Job Complexity Scale,and Psychological Meaningfulness Scale.Study design was cross-sectional.Hayes PROCESS macro analysis results indicate a higher work-family conflict with job complexity among the secondary school teachers.While psychological meaningfulness was not associated with work-family conflict,it moderated the link between job complexity and work-family conflict in secondary school teachers such that a meaningful work endorsement is associated with lower employee’s work-life conflict.Thesefindings point to the importance of job functions to quality of family life.The studyfindings also suggest a need for supporting psychological meaningfulness for healthy work related quality of family life based on balancing work and family role demands.
文摘Three copper(Ⅱ),nickel and cadmium(Ⅱ)complexes,namely[Cu_(2)(μ-H2dbda)2(phen)2]·2H_(2)O(1),[Ni(μ-H2dbda)(μ-bpb)(H_(2)O)2]n(2),and[Cd(μ-H2dbda)(μ-bpa)]n(3),have been constructed hydrothermally using H4dbda(4,4'-dihydroxy-[1,1'-biphenyl]-3,3'-dicarboxylic acid),phen(1,10-phenanthroline),bpb(1,4-bis(pyrid-4-yl)benzene),bpa(bis(4-pyridyl)amine),and copper,nickel and cadmium chlorides at 160℃.The products were isolated as stable crystalline solids and were characterized by IR spectra,elemental analyses,thermogravimetric analyses,and singlecrystal X-ray diffraction analyses.Single-crystal X-ray diffraction analyses revealed that three complexes crystallize in the monoclinic P21/n,tetragonal I42d,and orthorhombic P21212 space groups.The complexes exhibit molecular dimers(1)or 2D metal-organic networks(2 and 3).The catalytic performances in the Knoevenagel reaction of these complexes were investigated.Complex 1 exhibits an effective catalytic activity and excellent reusability as a heterogeneous catalyst in the Knoevenagel reaction at room temperature.CCDC:2463800,1;2463801,2;2463802,3.
