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
Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality an...Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.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
Intensity and variability of droughts are considered inIranduring the period 1951 to 2005. Four variables are considered: the Palmer Drought Severity Index (PDSI), the soil moisture, the temperature and the precipitat...Intensity and variability of droughts are considered inIranduring the period 1951 to 2005. Four variables are considered: the Palmer Drought Severity Index (PDSI), the soil moisture, the temperature and the precipitation (products used for the analysis are downloaded from the NCAR website). Link with the climatic indexLa Ninais also considered (NOAA downloadable products is used). The analysis is based on basic statistical approaches (correlation, linear regressions and Principal Component Analysis). The analysis shows that PDSI is highly correlated to the soil moisture and poorly correlated to the other variables—although the temperature in the warm season shows high correlation to the PDSI and that a severe drought was experienced during 1999-2002 inthe country.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
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
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
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isome...The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isomers,such as boiling point,of formation are found to be better measured by the ABS index than by the Randi,atom-bond connectivity(ABC),and sum-connectivity(SC)indices.One important source of information for researching the molecular structure is the bounds for its topological indices.The extrema of the ABS index of the line,total,and Mycielski graphs are calculated in this work.Moreover,the pertinent extremal graphs were illustrated.展开更多
The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims ...The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims to develop a hybrid model that combines the Fracture Aquifer Index(FAI)with the conventional GOD(Groundwater occurrence,Overall lithology,Depth to water table)method,to assess groundwater vulnerability in fractured aquifer.To develop the hybrid model,the classical GOD method was integrated with FAI to produce a single composite index.Each parameter within both GOD and FAI was scored,and a final index was calculated to delineate vulnerable areas.The results show that the study area can be classified into four vulnerability levels:Very low,low,moderate,and high,indicating that approximately 8%of the area exhibits very low vulnerability,29%has low vulnerability,25%falls into the moderate category,and 38%is considered highly vulnerable.The FAI-GOD model further incorporates fracture network characteristics.This refinement reduces the classification to three vulnerability classes:Low,medium,and high.The outcomes demonstrate that 46%of the area is highly vulnerable due to a dense concentration of fractures,while 17%represents an intermediate zone characterized by either shallow or deeper fractures.In contrast,37%corresponds to areas with lightly fractured rock,where the impact on vulnerability is minimal.Multivariate statistical analysis was employed using Principal Components Analysis(PCA)and Hierarchical Cluster Analysis(HCA)on 24 samples across six variables.The first three components account for over 76%of the total variance,reinforcing the significance of fracture dynamics in classifying vulnerability levels.The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low-and high-vulnerability zones,reflecting the dominant influence of fracture networks on aquifer sensitivity.While both indices use a five-class system,FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories,highlighting the critical role of fractures.A strong correlation(R2=0.94)between the GOD and FAI-GOD indices,demonstrated through second-order polynomial regression,confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution.This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.展开更多
Chain graphs are{2K_(2),C_(3),C_(5)}-free graphs.Balaban index and sum-Balaban index are two important topological indices.In this paper,we concentrate on the subclass of bicyclic connected chain graphs,identifying th...Chain graphs are{2K_(2),C_(3),C_(5)}-free graphs.Balaban index and sum-Balaban index are two important topological indices.In this paper,we concentrate on the subclass of bicyclic connected chain graphs,identifying the extremal graphs that exhibit the minimum or maximum Balaban index and sum-Balaban index within this class.Moreover,we provide a systematic ordering of all bicyclic connected chain graphs according to the magnitude of their Balaban index and sum-Balaban index.展开更多
This commentary critically appraises the study by Li et al which pioneered the exploration of the triglyceride-glucose(TyG)index as a prognostic marker in hepatitis B virus-related advanced hepatocellular carcinoma pa...This commentary critically appraises the study by Li et al which pioneered the exploration of the triglyceride-glucose(TyG)index as a prognostic marker in hepatitis B virus-related advanced hepatocellular carcinoma patients undergoing combined camrelizumab and lenvatinib therapy.While we acknowledge the study’s clinical relevance in proposing an easily accessible metabolic biomarker,we delve into the mechanistic plausibility linking insulin resistance to immunotherapy response and angiogenic inhibition.We further critically examine the methodological limitations,including the retrospective design,the populationspecific TyG cut-off value,and unaddressed metabolic confounders.We highlight the imperative for future research to validate its utility across diverse etiologies and treatment settings,and to unravel the underlying immunometabolic pathways.展开更多
BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an e...BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.展开更多
AIM:To explore the repeatability,reproducibility,and agreement in the measurement of the choroidal vascularity index(CVI)for different swept-source optical coherence tomography(OCT)devices and between OCT and OCT angi...AIM:To explore the repeatability,reproducibility,and agreement in the measurement of the choroidal vascularity index(CVI)for different swept-source optical coherence tomography(OCT)devices and between OCT and OCT angiography(OCTA)images.METHODS:Two swept-source OCT imaging systems,VG200I and Topcon DRI OCT Triton,were used to capture OCT and OCTA images in triplicate.The first and third images were taken by one operator,and the second image was taken by another operator.The built-in software was used to calculate the CVI from the OCTA images(CVI-OCTA),and a custom-designed algorithm was used to calculate the CVI from the OCT images(CVI-OCT).Repeatability and reproducibility were assessed with the intraclass correlation coefficient(ICC),and agreement between devices and between OCT and OCTA were evaluated with Bland-Altman analysis.RESULTS:Sixty-eight eyes from 35 adults(17 females)were included in the analysis.The average age of the participants was 23.6±2.3y,with an average spherical equivalent refraction of-3.08±2.47 D and an average AL of 25.21±1.20 mm.Both OCT devices demonstrated high repeatability and reproducibility in measuring the CVI-OCTA(all ICCs>0.894 across five choroidal regions)and CVI-OCT(all ICCs>0.838).Furthermore,the between-device agreement in measuring the CVI-OCT was good[mean difference(MD)ranging from-2.32%to-3.07%],but that in measuring the CVI-OCTA was poor(MD,1.48%to-7.43%).Additionally,the between-imaging agreement(CVI-OCTA versus CVI-OCT)was poor for both devices(Triton,MD,6.05%to 12.68%;VG200I,MD,6.67%to 12.09%).CONCLUSION:Both OCT devices and the two analytical methods demonstrate good stability.The inter-device consistency of CVI-OCT is good,while the inter-device consistency of CVI-OCTA and the consistency between the two analytical methods in the same device are both poor.展开更多
Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divide...Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.展开更多
BACKGROUND:Rapid identification of patients at risk of clinical deterioration(in-hospital mortality) in emergency settings is essential for timely and appropriate care.Existing prognostic scores,such as the Acute Phys...BACKGROUND:Rapid identification of patients at risk of clinical deterioration(in-hospital mortality) in emergency settings is essential for timely and appropriate care.Existing prognostic scores,such as the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),Simplified Acute Physiology Score 3(SAPS 3),Sequential Organ Failure Assessment(SOFA),and National Early Warning Score 2(NEWS 2),have limitations in emergency scenarios,particularly in resource-limited settings.We aimed to develop a simple and efficient tool tailored to the Brazilian healthcare system.METHODS:This retrospective,multicenter,cohort study analyzed data from 50,709 adult patients admitted to 12 hospitals in southern and southeastern Brazil between 2019 and 2020.The BRASIL score(Brazilian Risk Assessment Severity Index and Length of stay) was constructed using demographic and clinical variables available at admission.Logistic regression was used to determine the weight of each variable,and each variable was assigned a point value based on its β-coefficient and clinical relevance,with thresholds defined according to established medical cutoffs and statistical performance.The score's predictive accuracy was validated using the area under the receiver operating characteristic curve(AUC) with comparative analysis against NEWS 2.RESULTS:The BRASIL score,including age,sex,respiratory rate,heart rate,oxygen saturation,blood pressure,and body temperature,was derived through variables independently associated with in-hospital mortality in a multicenter cohort.The total score was stratified into three risk categories — low(0–3 points),moderate(4–7 points),and high(>7 points) — using observed inflection points in mortality distribution to optimize discrimination.This stratification demonstrated a stepwise increase in mortality rates across categories and the discriminatory performance,with an overall AUC of 0.743(95% CI:0.726–0.761).Compared to NEWS 2(AUC 0.697,95% CI:0.683–0.711),the BRASIL score offered superior early risk identification,supporting timely clinical decisionmaking and resource allocation in the emergency setting.CONCLUSION:The BRASIL score is a novel tool for predicting in-hospital mortality in emergency departments.Its predictive performance and ease of use suggest that it has the potential to improve patient outcomes.展开更多
The associations of co-exposure to per-and polyfluoroalkyl substances(PFAS),polycyclic aromatic hydrocarbons(PAHs),and metals with children’s glycometabolism and the underlying mechanism of immune inflammation are la...The associations of co-exposure to per-and polyfluoroalkyl substances(PFAS),polycyclic aromatic hydrocarbons(PAHs),and metals with children’s glycometabolism and the underlying mechanism of immune inflammation are largely unclear.We conducted a longitudinal panel study to explore the effects of individual and mixture of 27 contaminants on an emerging surrogate indicator of insulin resistance,triglyceride-glucose index(TyG),and the mediation of immunoglobulins and cytokines in children aged 4–6 years and 11–13 years.The results showed robust associations of perfluorooctanoic acid(PFOA),perfluorononanoic acid(PFNA),perfluoroundecanoic acid(PFUnDA),and arsenic(As)with elevated TyG.The interaction of PFNA with PFOA was significant,showing a synergistic effect on TyG.And combined association of each pair of PFOA,PFNA,and As with TyG were enhanced.Meanwhile,the effect of contaminants mixture on TyG was significant for polyfluoroalkyl substances(PFAS)mixture,of which 6:2 Chlorinated polyfluorinated ether sulfonates(Cl-PFESA),PFNA,and PFUnDA weighted more than others.Notably,contaminants were related to immune globulins and cytokines,of which chemokine ligand(CCL)4,interleukin(IL)-1β,IL-9,and tumor necrosis factor(TNF)-α significantly mediated associations of PFOA,PFNA,and PFUnDA with TyG.Accordingly,PFAS and metals were individually and jointly associated with TyG elevation,with 6:2 Cl-PFESA,PFNA,and PFUnDA contributing the most,and CCL4,IL-1β,IL-9,and TNF-α might be the underlying mediators in children.展开更多
Gradient refractive index(GRIN)metalenses are increasingly valued in high-frequency communication due to their exceptional radiation performance.Ceramics with high dielectric constants and low dielectric losses are id...Gradient refractive index(GRIN)metalenses are increasingly valued in high-frequency communication due to their exceptional radiation performance.Ceramics with high dielectric constants and low dielectric losses are ideal candidates for GRIN metalenses.Digital light processing(DLP)3D printing provides a feasible and efficient approach for manufacturing ceramic GRIN metalenses.However,the scattering of ultraviolet(UV)light by ceramic particles in the slurry reduces the printing accuracy of DLP technology,making it difficult to achieve the intricate structural features required for GRIN metalenses in high-frequency communication.In this work,we propose an approach to improve printing accuracy by optimizing the ceramic slurry composition and implementing a dimensional compensation design strategy.Utilizing geometric optics and the S-parameter inversion method,we design a GRIN metalens consisting of two distinct types of subwavelength unit cells(Y-shaped and circular hole geometries)with a minimum feature size of 160μm.Through a refined slurry formulation and precise design parameter compensation,high-fidelity ceramic GRIN metalenses are successfully fabricated.The fabricated metalens exhibits a maximum gain enhancement of 18.4 dBi and a deflection angle of±30°over a bandwidth of 37.84% in the W-band(75-110 GHz).The highly directional far-field beam radiation and efficient beam steering capabilities highlight the potential of ceramic GRIN metalenses for applications in satellite communications,radar systems,and other high-frequency technologies.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.
基金Under the auspices of Key Projects of the National Social Science Fund(No.16AJL015)Youth Project of Natural Science Foundation of Jiangsu Province(No.BK20170440)+1 种基金Open Foundation of Key Laboratory of Watershed Geographical Science(No.WSGS2017004)Project of Nantong Key Laboratory(No.CP12016005)
文摘Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.
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.
文摘Intensity and variability of droughts are considered inIranduring the period 1951 to 2005. Four variables are considered: the Palmer Drought Severity Index (PDSI), the soil moisture, the temperature and the precipitation (products used for the analysis are downloaded from the NCAR website). Link with the climatic indexLa Ninais also considered (NOAA downloadable products is used). The analysis is based on basic statistical approaches (correlation, linear regressions and Principal Component Analysis). The analysis shows that PDSI is highly correlated to the soil moisture and poorly correlated to the other variables—although the temperature in the warm season shows high correlation to the PDSI and that a severe drought was experienced during 1999-2002 inthe country.
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 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 National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金Supported by Ningbo NSF(No.2021J234)Zhejiang Provincial Philosophy and Social Sciences Planning Project(No.24NDJC057YB)。
文摘The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isomers,such as boiling point,of formation are found to be better measured by the ABS index than by the Randi,atom-bond connectivity(ABC),and sum-connectivity(SC)indices.One important source of information for researching the molecular structure is the bounds for its topological indices.The extrema of the ABS index of the line,total,and Mycielski graphs are calculated in this work.Moreover,the pertinent extremal graphs were illustrated.
文摘The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims to develop a hybrid model that combines the Fracture Aquifer Index(FAI)with the conventional GOD(Groundwater occurrence,Overall lithology,Depth to water table)method,to assess groundwater vulnerability in fractured aquifer.To develop the hybrid model,the classical GOD method was integrated with FAI to produce a single composite index.Each parameter within both GOD and FAI was scored,and a final index was calculated to delineate vulnerable areas.The results show that the study area can be classified into four vulnerability levels:Very low,low,moderate,and high,indicating that approximately 8%of the area exhibits very low vulnerability,29%has low vulnerability,25%falls into the moderate category,and 38%is considered highly vulnerable.The FAI-GOD model further incorporates fracture network characteristics.This refinement reduces the classification to three vulnerability classes:Low,medium,and high.The outcomes demonstrate that 46%of the area is highly vulnerable due to a dense concentration of fractures,while 17%represents an intermediate zone characterized by either shallow or deeper fractures.In contrast,37%corresponds to areas with lightly fractured rock,where the impact on vulnerability is minimal.Multivariate statistical analysis was employed using Principal Components Analysis(PCA)and Hierarchical Cluster Analysis(HCA)on 24 samples across six variables.The first three components account for over 76%of the total variance,reinforcing the significance of fracture dynamics in classifying vulnerability levels.The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low-and high-vulnerability zones,reflecting the dominant influence of fracture networks on aquifer sensitivity.While both indices use a five-class system,FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories,highlighting the critical role of fractures.A strong correlation(R2=0.94)between the GOD and FAI-GOD indices,demonstrated through second-order polynomial regression,confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution.This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.
文摘Chain graphs are{2K_(2),C_(3),C_(5)}-free graphs.Balaban index and sum-Balaban index are two important topological indices.In this paper,we concentrate on the subclass of bicyclic connected chain graphs,identifying the extremal graphs that exhibit the minimum or maximum Balaban index and sum-Balaban index within this class.Moreover,we provide a systematic ordering of all bicyclic connected chain graphs according to the magnitude of their Balaban index and sum-Balaban index.
文摘This commentary critically appraises the study by Li et al which pioneered the exploration of the triglyceride-glucose(TyG)index as a prognostic marker in hepatitis B virus-related advanced hepatocellular carcinoma patients undergoing combined camrelizumab and lenvatinib therapy.While we acknowledge the study’s clinical relevance in proposing an easily accessible metabolic biomarker,we delve into the mechanistic plausibility linking insulin resistance to immunotherapy response and angiogenic inhibition.We further critically examine the methodological limitations,including the retrospective design,the populationspecific TyG cut-off value,and unaddressed metabolic confounders.We highlight the imperative for future research to validate its utility across diverse etiologies and treatment settings,and to unravel the underlying immunometabolic pathways.
基金Supported by Inter Disciplinary Direction Cultivation Project of Hunan University of Chinese Medicine,No.2025JC01032025 Hunan Province Science and Technology Innovation Plan Project,No.2025RC9012+2 种基金2022"Unveiling and Leading"Project of Discipline Construction at Hunan University of Chinese Medicine,No.22JBZ044Changsha Municipal Natural Science Foundation,No.kq2402174Hunan Provincial Science Popularization Fund Project,No.2025ZK4223.
文摘BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.
基金Supported by the National Key Research and Development Program of China(No.2022YFC3502503)the Medical and Health Science and Technology Project of the Zhejiang Provincial Health Commission of China(No.2022PY072).
文摘AIM:To explore the repeatability,reproducibility,and agreement in the measurement of the choroidal vascularity index(CVI)for different swept-source optical coherence tomography(OCT)devices and between OCT and OCT angiography(OCTA)images.METHODS:Two swept-source OCT imaging systems,VG200I and Topcon DRI OCT Triton,were used to capture OCT and OCTA images in triplicate.The first and third images were taken by one operator,and the second image was taken by another operator.The built-in software was used to calculate the CVI from the OCTA images(CVI-OCTA),and a custom-designed algorithm was used to calculate the CVI from the OCT images(CVI-OCT).Repeatability and reproducibility were assessed with the intraclass correlation coefficient(ICC),and agreement between devices and between OCT and OCTA were evaluated with Bland-Altman analysis.RESULTS:Sixty-eight eyes from 35 adults(17 females)were included in the analysis.The average age of the participants was 23.6±2.3y,with an average spherical equivalent refraction of-3.08±2.47 D and an average AL of 25.21±1.20 mm.Both OCT devices demonstrated high repeatability and reproducibility in measuring the CVI-OCTA(all ICCs>0.894 across five choroidal regions)and CVI-OCT(all ICCs>0.838).Furthermore,the between-device agreement in measuring the CVI-OCT was good[mean difference(MD)ranging from-2.32%to-3.07%],but that in measuring the CVI-OCTA was poor(MD,1.48%to-7.43%).Additionally,the between-imaging agreement(CVI-OCTA versus CVI-OCT)was poor for both devices(Triton,MD,6.05%to 12.68%;VG200I,MD,6.67%to 12.09%).CONCLUSION:Both OCT devices and the two analytical methods demonstrate good stability.The inter-device consistency of CVI-OCT is good,while the inter-device consistency of CVI-OCTA and the consistency between the two analytical methods in the same device are both poor.
基金Supported by the National Natural Science Foundation of China(Grant No.12071092)Guangdong Basic and Applied Basic Research Foundation(Grant No.2025A1515012072)+1 种基金the Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH051298)the Scientific Research Foundation of Bozhou University(Grant No.BYKQ202419).
文摘Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.
文摘BACKGROUND:Rapid identification of patients at risk of clinical deterioration(in-hospital mortality) in emergency settings is essential for timely and appropriate care.Existing prognostic scores,such as the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),Simplified Acute Physiology Score 3(SAPS 3),Sequential Organ Failure Assessment(SOFA),and National Early Warning Score 2(NEWS 2),have limitations in emergency scenarios,particularly in resource-limited settings.We aimed to develop a simple and efficient tool tailored to the Brazilian healthcare system.METHODS:This retrospective,multicenter,cohort study analyzed data from 50,709 adult patients admitted to 12 hospitals in southern and southeastern Brazil between 2019 and 2020.The BRASIL score(Brazilian Risk Assessment Severity Index and Length of stay) was constructed using demographic and clinical variables available at admission.Logistic regression was used to determine the weight of each variable,and each variable was assigned a point value based on its β-coefficient and clinical relevance,with thresholds defined according to established medical cutoffs and statistical performance.The score's predictive accuracy was validated using the area under the receiver operating characteristic curve(AUC) with comparative analysis against NEWS 2.RESULTS:The BRASIL score,including age,sex,respiratory rate,heart rate,oxygen saturation,blood pressure,and body temperature,was derived through variables independently associated with in-hospital mortality in a multicenter cohort.The total score was stratified into three risk categories — low(0–3 points),moderate(4–7 points),and high(>7 points) — using observed inflection points in mortality distribution to optimize discrimination.This stratification demonstrated a stepwise increase in mortality rates across categories and the discriminatory performance,with an overall AUC of 0.743(95% CI:0.726–0.761).Compared to NEWS 2(AUC 0.697,95% CI:0.683–0.711),the BRASIL score offered superior early risk identification,supporting timely clinical decisionmaking and resource allocation in the emergency setting.CONCLUSION:The BRASIL score is a novel tool for predicting in-hospital mortality in emergency departments.Its predictive performance and ease of use suggest that it has the potential to improve patient outcomes.
基金supported by the National Key Research and Development Program of China(No.2022YFC3702702)the National Natural Science Foundation of China(No.82404295)the Fundamental Research Funds for the Central Universities,HUST(No.2020kfyXJJS058).
文摘The associations of co-exposure to per-and polyfluoroalkyl substances(PFAS),polycyclic aromatic hydrocarbons(PAHs),and metals with children’s glycometabolism and the underlying mechanism of immune inflammation are largely unclear.We conducted a longitudinal panel study to explore the effects of individual and mixture of 27 contaminants on an emerging surrogate indicator of insulin resistance,triglyceride-glucose index(TyG),and the mediation of immunoglobulins and cytokines in children aged 4–6 years and 11–13 years.The results showed robust associations of perfluorooctanoic acid(PFOA),perfluorononanoic acid(PFNA),perfluoroundecanoic acid(PFUnDA),and arsenic(As)with elevated TyG.The interaction of PFNA with PFOA was significant,showing a synergistic effect on TyG.And combined association of each pair of PFOA,PFNA,and As with TyG were enhanced.Meanwhile,the effect of contaminants mixture on TyG was significant for polyfluoroalkyl substances(PFAS)mixture,of which 6:2 Chlorinated polyfluorinated ether sulfonates(Cl-PFESA),PFNA,and PFUnDA weighted more than others.Notably,contaminants were related to immune globulins and cytokines,of which chemokine ligand(CCL)4,interleukin(IL)-1β,IL-9,and tumor necrosis factor(TNF)-α significantly mediated associations of PFOA,PFNA,and PFUnDA with TyG.Accordingly,PFAS and metals were individually and jointly associated with TyG elevation,with 6:2 Cl-PFESA,PFNA,and PFUnDA contributing the most,and CCL4,IL-1β,IL-9,and TNF-α might be the underlying mediators in children.
基金financial support by the National Key Research and Development Program of China(No.2023YFB4605400)the National Natural Science Foundation of China(No.12472152)the Department of Science and Technology of Guangdong Province(No.2019QN01Z438)。
文摘Gradient refractive index(GRIN)metalenses are increasingly valued in high-frequency communication due to their exceptional radiation performance.Ceramics with high dielectric constants and low dielectric losses are ideal candidates for GRIN metalenses.Digital light processing(DLP)3D printing provides a feasible and efficient approach for manufacturing ceramic GRIN metalenses.However,the scattering of ultraviolet(UV)light by ceramic particles in the slurry reduces the printing accuracy of DLP technology,making it difficult to achieve the intricate structural features required for GRIN metalenses in high-frequency communication.In this work,we propose an approach to improve printing accuracy by optimizing the ceramic slurry composition and implementing a dimensional compensation design strategy.Utilizing geometric optics and the S-parameter inversion method,we design a GRIN metalens consisting of two distinct types of subwavelength unit cells(Y-shaped and circular hole geometries)with a minimum feature size of 160μm.Through a refined slurry formulation and precise design parameter compensation,high-fidelity ceramic GRIN metalenses are successfully fabricated.The fabricated metalens exhibits a maximum gain enhancement of 18.4 dBi and a deflection angle of±30°over a bandwidth of 37.84% in the W-band(75-110 GHz).The highly directional far-field beam radiation and efficient beam steering capabilities highlight the potential of ceramic GRIN metalenses for applications in satellite communications,radar systems,and other high-frequency technologies.
