In this paper,work was conducted to reveal electrical tree behaviors（initiation and propagation）of silicone rubber（SIR） under an impulse voltage with high temperature.Impulse frequencies ranging from 10 Hz to 1 k ...In this paper,work was conducted to reveal electrical tree behaviors（initiation and propagation）of silicone rubber（SIR） under an impulse voltage with high temperature.Impulse frequencies ranging from 10 Hz to 1 k Hz were applied and the temperature was controlled between 30 °C and 90 °C.Experimental results show that tree initiation voltage decreases with increasing pulse frequency,and the descending amplitude is different in different frequency bands.As the pulse frequency increases,more frequent partial discharges occur in the channel,increasing the tree growth rate and the final shape intensity.As for temperature,the initiation voltage decreases and the tree shape becomes denser as the temperature gets higher.Based on differential scanning calorimetry results,we believe that partial segment relaxation of SIR at high temperature leads to a decrease in the initiation voltage.However,the tree growth rate decreases with increasing temperature.Carbonization deposition in the channel under high temperature was observed under microscope and proven by Raman analysis.Different tree growth models considering tree channel characteristics are proposed.It is believed that increasing the conductivity in the tree channel restrains the partial discharge,holding back the tree growth at high temperature.展开更多
This paper presents a comprehensive analysis of the insulation breakdown phenomenon in epoxy resin insulation induced by the degradation of electrical trees,with reference to both domestic and international studies.It...This paper presents a comprehensive analysis of the insulation breakdown phenomenon in epoxy resin insulation induced by the degradation of electrical trees,with reference to both domestic and international studies.It elucidates the mechanisms of electrical tree initiation and degradation in epoxy resins by drawing on the theories of space charge accumulation and migration,local electric field formation,ultraviolet radiation,and mechanical stress chain breakage.The paper also discusses the operational conditions of the electrical equipment,detailing the effects of superimposed electric fields,temperature gradients,mechanical stresses,magnetic gradients,and humid environments on the insulation degradation of the epoxy resin.Furthermore,it summarizes the correlation between tree growth characteristics and charge transport behavior under the influence of multiple physical fields.To enhance the insulation performance of epoxy resins and ensure the safe and reliable operation of electrical equipment,methods to inhibit electrical tree formation are discussed.These methods include the regulation of the manufacturing process,inorganic doping,and the use of self-repairing materials.The inhibition mechanisms are analyzed at the molecular configuration level and microstructural scale,and from a macroscopic standpoint.This study aimed to provide recommendations for future research on the growth and suppression of electrical trees in epoxy resin based on the current state of knowledge.展开更多
When making assessments of forest resources,there is nearly ubiquitous interest in quantifying current status and trends in tree biomass and carbon stocks.While important at various spatial scales,typical estimations ...When making assessments of forest resources,there is nearly ubiquitous interest in quantifying current status and trends in tree biomass and carbon stocks.While important at various spatial scales,typical estimations pertinent to broad forest management and policy issues are conducted for large areas such as state,regional,and national perspectives.These assessments are usually accomplished using large-area forest inventory data collected by National Forest Inventory(NFI)programs.While NFI efforts commonly collect size data for individual trees,there is often limited information for tree seedlings,e.g.,frequency by species.To fully describe the tree population across the entire range of sizes present,this study proposes methods to predict individual seedling groundline diameter and height using models developed from trees having a diameter at breast height(DBH)less than 7.62 cm.These attributes are subsequently used for the prediction of seedling stem volume,total aboveground biomass,and carbon content.The results suggest a smooth transition in tree attributes as size increases to where direct measurement of individual trees and prediction of their volume,biomass,and carbon are implemented as part of standard inventory protocols.Analyses including the full spectrum of tree sizes show that seedlings contribute roughly 0.6%–0.7%of the total tree volume/mass.This additional suite of information provides opportunities for more holistic assessments across the full spectrum of the tree resource or for specialized subdomains that include the seedling component.展开更多
This study investigated biomass allocation in young stands of European beech(Fagus sylvatica L.)and Norway spruce(Picea abies(L.)Karst.)across 31 forest sites in the Western Carpathians,Slovakia.A total of 541 trees a...This study investigated biomass allocation in young stands of European beech(Fagus sylvatica L.)and Norway spruce(Picea abies(L.)Karst.)across 31 forest sites in the Western Carpathians,Slovakia.A total of 541 trees aged 2–10 years,originating from natural regeneration and planting,were destructively sampled to quantify biomass in four components:foliage,branches,stems,and roots.Generalized non-linear least squares(GNLS)models with a weighing variance function outperformed log-transformed seemingly unrelated regression(SUR)models in terms of accuracy and robustness,especially for foliage and branch biomass.When using height as the predictor,SUR models tended to underestimate biomass in planted beech,leading to notable underprediction of aboveground and total biomass.Biomass allocation patterns varied significantly by species and regeneration origin.Using a non-linear system of equations and component ratio modelling,we found out that planted spruce displayed low variability and a consistent dominance of needle biomass,while naturally regenerated beech showed greater variability and a higher proportion of stem biomass,reflecting stronger competition-driven vertical growth.Interspecific differences in total biomass were more pronounced when using tree height,with spruce generally exhibiting greater biomass than beech at equivalent heights.Overall,stem base diameter marginally outperformed tree height as a predictor of biomass.However,tree height-based models showed strong performance and are particularly suitable for integration with remote sensing applications.These findings can directly support forest managers and modellers in comparing regeneration methods and biomass estimation approaches for early-stage stand development,carbon accounting,and remote sensing calibration.展开更多
Allometric equations are fundamental tools in ecological research and forestry management,widely used for estimating above-ground biomass and production,serving as the core foundations of dynamic vegetation models.Usi...Allometric equations are fundamental tools in ecological research and forestry management,widely used for estimating above-ground biomass and production,serving as the core foundations of dynamic vegetation models.Using global datasets from Tallo(a tree allometry and crown architecture database encompassing thousands of species)and TRY(a plant traits database),we fit B ayesian hierarchical models with three alternative functional forms(powerlaw,generalized Michaelis-Menten(gMM),and Weibull)to characterize how diameter at breast height(DBH),tree height(H),and crown radius(CR)scale with and without wood density as a species-level predictor.Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups,whereas the CR-DBH relationship was best predicted by a power-law function in angiosperms and by the gMM function in gymnosperms.Although including wood density did not significantly improve predictive performance,it revealed important ecological trade-offs:lighter-wood angiosperms achieve taller mature heights more rapidly,and denser wood promotes wider crown expansion across clades.We also found that accurately estimating DBH required considering both height and crown size,highlighting how these variables together distinguish trees of similar height but differing trunk diameters.Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density,though not always predictive at broad scales,helps illuminate the biomechanical and ecological constraints underlying diverse tree architectures.These findings offer practical pathways for integrating height-and crown-based metrics into existing carbon monitoring programs worldwide.展开更多
Soil organic carbon in forest affects nutrient availability,microbial processes,and organic matter inputs.Dominant tree species have increasingly shifted from ectomycorrhizal to arbuscular mycorrhizal associations in ...Soil organic carbon in forest affects nutrient availability,microbial processes,and organic matter inputs.Dominant tree species have increasingly shifted from ectomycorrhizal to arbuscular mycorrhizal associations in subtropical forests.However,the consequences of this shift for soil organic carbon is poorly understood.To address this,a field study was conducted across a natural gradient of arbuscular tree associations to investigate how different mycorrhizal associations affect soil organic carbon quantity,composition,chemical stability,and related soil properties.Soil organic carbon fractions,functional groups,microbial enzyme activities were analyzed.Results showed that increasing arbuscular mycorrhizal dominance was associated with declines in total soil organic carbon,particularly in recalcitrant and aromatic carbon forms.Ectomycorrhizaldominated forests exhibited higher nitrogen availability and elevated nitrogen-hydrolyzing enzyme activity,suggesting enhanced nitrogen acquisition strategies that suppress soil organic carbon decomposition and promote carbon retention.These findings indicate that mycorrhizal-mediated shifts in tree composition may significantly alter soil carbon sequestration potential.Incorporating mycorrhizal functional traits into forest management and carbon modeling could improve predictions of soil organic carbon responses under future environmental change.展开更多
Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees rem...Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.展开更多
Peri-urban plantations in the Mediterranean are often degraded due to human inactivity and climate change,leading to a loss of ecosystem services and biodiversity.This study investigates the impact of different thinni...Peri-urban plantations in the Mediterranean are often degraded due to human inactivity and climate change,leading to a loss of ecosystem services and biodiversity.This study investigates the impact of different thinning practices on carbon sequestration and tree stability in a degraded periurban plantation in the Italian Apennines,six years after thinning.Three treatments were compared:(a)moderate thinning from below(-25%biomass),representing the typical practice;(b)intense selective thinning(-35%biomass),representing an innovative approach;and(c)no management as the control.Growth projections were used to estimate carbon recovery for these treatments,based on site-specific models calibrated with real data.The results show that both thinning approaches increased carbon sequestration over time,with the innovative thinning achieving a 7%higher annual carbon sequestration rate than traditional thinning and 8%more than the control.Estimated payback times were9 years for recovering the harvested volume in both thinning approaches,10 years for innovative thinning to surpass traditional thinning,17 years for innovative thinning to surpass the control,and 24 years for traditional thinning to surpass the control.Additionally,tree mechanical stability improved significantly in both thinning treatments after two years,with further increases observed in the innovative thinning group after six years.These results suggest that selective thinning can accelerate forest recovery and carbon sequestration,especially in areas with high stem density,where it can reduce the negative impacts of tree mortality and deadwood accumulation.However,careful planning is required to mitigate potential short-term stability is sues,particularly in challenging environments(e.g.,windy conditions,steep slopes).Forest management strategies should therefore aim to balance growth,carbon storage,and tree stability,considering both long-term sustainability and local environmental conditions.The findings are particularly relevant for current climate change mitigation strategies,emphasizing that thinning should be carefully tailored to forest type and conditions to maximize benefits in carbon credit generation and sustainable forest management practices.展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
Old-growth forests uniquely support biodiversity while serving as some of the planet's most important carbon stocks.The influence of tree and stand age on carbon flux dynamics remains debated—an urgent question a...Old-growth forests uniquely support biodiversity while serving as some of the planet's most important carbon stocks.The influence of tree and stand age on carbon flux dynamics remains debated—an urgent question as climate-driven disturbances may reshape forest age structures and in situ carbon storage.To clarify these relationships in Fagus sylvatica,systems,we examined a unique dataset of 3,503 tree ring series from 190 plots across some of the best preserved old-growth forests from five southern European countries.By employing a dendrochronological approach and integrating key environmental variables,including elevation,slope,temperature,and the presence of large-diameter trees(≥60 cm),we analyzed the complex relationships between tree/stand age within a plot(represented by plot-level mean values,hereafter“stand age”)and aboveground carbon stock across live,standing,and lying deadwood pools.The average stand age was 220 years,with 230 tC⋅ha^(-1) of carbon stored in aboveground biomass and necromass.We found a positive correlation between age and carbon storage at both the individual tree and plot levels.Notably,the presence of large-diameter trees was the strongest indicator of carbon stock,with carbon accumulation peaking at about 30%large-tree stems proportion before stabilising,while younger beech trees(below 100 years old)had a smaller contribution to carbon storage.We found no evidence of a decline in carbon stock with advancing stand age across the studied sites.Despite the ecological importance of old-growth forests,many of them remain unprotected and are disappearing across Europe.Our findings highlight the importance of preserving old-growth forests to maximize their role as long-term ecosystem carbon reservoirs.展开更多
Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to...Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to ongoing debate in this field.Moreover,gaps remain in understanding the climatic mechanisms driving increased tree growth variability,particularly for species simultaneously limited by multiple climate factors.In this study,we assessed the temporal trends in variability of Picea purpurea radial growth and its linkage with growth-climate sensitivity utilizing dendrochronological methods.Our results revealed a significant increase in P.purpurea radial growth variability from 1960 to 2020,as indicated by continuous rises in the standard deviation,coefficient of variation,and mean sensitivity of tree-ring width indices.The increased frequency of extreme growth declines further supported this finding.Furthermore,moisture condition in July was identified as a key limiting factor of P.purpurea growth.Notably,the strengthening relationship between tree-ring width indices and vapor pressure deficit(VPD)suggests that the moisture sensitivity for P.purpurea growth has increased over the period 1960-2020.This enhanced sensitivity to VPD,whose interannual variability has also increased synchronously,may have contributed to the rise in P.purpurea growth variability.Additionally,the maximum temperature in May was positively correlated with P.purpurea growth;however,there is little evidence that this factor contributed to the observed increase in growth variability.These findings provide new insights into P.purpurea growth trends and improve our understanding of the potential future impacts of climate change on forest ecosystems.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In...We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.展开更多
In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false ...In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.展开更多
Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and ...Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and diversity on leaf litter decomposition in forests remains understudied.By controlling for macroclimate,soil properties,and litter substrate in a mature common garden,we investigated whether a three-month tea bag incubation of standardized green and rooibos tea substrate is driven by canopy tree species characteristics and diversity.Our study hypothesized two primary pathways:a chemical engineering effect,where trees alter soil properties and decomposer communities through litter input,and a physical engineering effect,where tree canopy structure modulates the local microclimate.The results showed that even under uniform macroclimatic and initial soil conditions,mass loss rates varied widely for green tea(27.4%–73.2%)and rooibos tea(6.1%–34.7%),comparable as found in other research between distinct biomes.While substrate quality was the dominant factor,both engineering pathways and,to a minor extent,tree diversity modulated mass losses.For green tea,tree chemical and physical characteristics seemed equally important,while the physical environment showed an increased importance for rooibos.Incubation depth played a key role,where forest floor decomposition rates are more susceptible to temporal climate variations,and soil-layer decomposition rates are less susceptible to climate variations and more determined by tree species identity.Our findings suggest that tea bag experiments focusing solely on topsoil burial may underestimate processes in the forest floor and the mineralorganic boundary layer.This study underscores the critical role of litter substrate quality in decomposition while demonstrating that tree community composition and the associated herbaceous layer,through both chemical and physical engineering pathways,strongly modulate decomposition rates.展开更多
Urban forests are highly multifunctional and provide numerous ecological functions.Plant functional traits individually or jointly influence the ecological multifunctionality of tree species(TS-EMF)and can also modify...Urban forests are highly multifunctional and provide numerous ecological functions.Plant functional traits individually or jointly influence the ecological multifunctionality of tree species(TS-EMF)and can also modify TSEMF in response to environmental changes.However,there has been limited exploration of multitrait combinations for predicting TS-EMF across seasons and of trait thresholds that enhance TS-EMF.Here,for 10 dominant tree species in urban forests of Northeast China,14 traits were measured and four aboveground and three belowground ecological functions assessed in three seasons.Ecological functions and TS-EMF differed significantly throughout the seasons(P<0.05).Synergistic relationships were found between carbon sequestration and oxygen release,between cooling and humidification,and between organic carbon accumulation and nutrient cycling.Notably,aboveground multifunctionality played a leading role in TS-EMF.With seasonal changes,resource allocation shifted toward traits related to resource acquisition rather than conservation to maintain TS-EMF.The combination of traits that predicted TS-EMF varied by type,accounting for up to 66.45%of the variation.TS-EMF was primarily driven by leaf structure in spring and by nutrient accumulation in autumn.Leaf carbon content(LCC)consistently served as a stabilizing factor for predicting TS-EMF across seasons.At 36.5-36.8 mg g^(-1),LCC had its optimal effect on TS-EMF.Other traits in combination that positively influence total TS-EMF include leaf nitrogen content(3.43-3.45 mg g^(-1)),leaf phosphorus content(0.80-0.83 mg g^(-1)),and leaf area(65.86-68.43 cm^(2)).Within these specified trait thresholds,Morus alba and Quercus mongolica were identified as key species.These findings suggest that the trade-off between various ecological functions can be managed by altering plant traits across seasons.This approach could provide a theoretical foundation for enhancing the TS-EMF of urban forests through trait-based management,offering practical guidance for selecting tree species.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiment...Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.展开更多
Mood-driven purchases of both goods and services are on the rise,emerging as the new engine driving consumption.MINIATURE decorative green banana trees called“No Anxiety”(a word play on the Chinese words“don’t be ...Mood-driven purchases of both goods and services are on the rise,emerging as the new engine driving consumption.MINIATURE decorative green banana trees called“No Anxiety”(a word play on the Chinese words“don’t be a green banana”),blind boxes,mountain hiking companions,and AI chat pals-many products and services that were dismissed as useless or worthless years ago are now gaining popularity among young consumers.Behind this trend is the increase in consumer spending,driven more by emotions than practical needs.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
基金supported in part by National Basic Research Program of China(973 Project)(No.2014CB239501)National Natural Science Foundation of China(Nos.51707100,51377089)+1 种基金State Key Laboratory of Electrical Insulation and Power Equipment(No.EIPE16208)China Postdoctoral Science Foundation(No.2016M591176)
文摘In this paper,work was conducted to reveal electrical tree behaviors（initiation and propagation）of silicone rubber（SIR） under an impulse voltage with high temperature.Impulse frequencies ranging from 10 Hz to 1 k Hz were applied and the temperature was controlled between 30 °C and 90 °C.Experimental results show that tree initiation voltage decreases with increasing pulse frequency,and the descending amplitude is different in different frequency bands.As the pulse frequency increases,more frequent partial discharges occur in the channel,increasing the tree growth rate and the final shape intensity.As for temperature,the initiation voltage decreases and the tree shape becomes denser as the temperature gets higher.Based on differential scanning calorimetry results,we believe that partial segment relaxation of SIR at high temperature leads to a decrease in the initiation voltage.However,the tree growth rate decreases with increasing temperature.Carbonization deposition in the channel under high temperature was observed under microscope and proven by Raman analysis.Different tree growth models considering tree channel characteristics are proposed.It is believed that increasing the conductivity in the tree channel restrains the partial discharge,holding back the tree growth at high temperature.
基金Supported by the Smart Gird-National Science and Technology Major Project(2024ZD0802401)the National Natural Science Foundation of China(52307181,52220105002).
文摘This paper presents a comprehensive analysis of the insulation breakdown phenomenon in epoxy resin insulation induced by the degradation of electrical trees,with reference to both domestic and international studies.It elucidates the mechanisms of electrical tree initiation and degradation in epoxy resins by drawing on the theories of space charge accumulation and migration,local electric field formation,ultraviolet radiation,and mechanical stress chain breakage.The paper also discusses the operational conditions of the electrical equipment,detailing the effects of superimposed electric fields,temperature gradients,mechanical stresses,magnetic gradients,and humid environments on the insulation degradation of the epoxy resin.Furthermore,it summarizes the correlation between tree growth characteristics and charge transport behavior under the influence of multiple physical fields.To enhance the insulation performance of epoxy resins and ensure the safe and reliable operation of electrical equipment,methods to inhibit electrical tree formation are discussed.These methods include the regulation of the manufacturing process,inorganic doping,and the use of self-repairing materials.The inhibition mechanisms are analyzed at the molecular configuration level and microstructural scale,and from a macroscopic standpoint.This study aimed to provide recommendations for future research on the growth and suppression of electrical trees in epoxy resin based on the current state of knowledge.
文摘When making assessments of forest resources,there is nearly ubiquitous interest in quantifying current status and trends in tree biomass and carbon stocks.While important at various spatial scales,typical estimations pertinent to broad forest management and policy issues are conducted for large areas such as state,regional,and national perspectives.These assessments are usually accomplished using large-area forest inventory data collected by National Forest Inventory(NFI)programs.While NFI efforts commonly collect size data for individual trees,there is often limited information for tree seedlings,e.g.,frequency by species.To fully describe the tree population across the entire range of sizes present,this study proposes methods to predict individual seedling groundline diameter and height using models developed from trees having a diameter at breast height(DBH)less than 7.62 cm.These attributes are subsequently used for the prediction of seedling stem volume,total aboveground biomass,and carbon content.The results suggest a smooth transition in tree attributes as size increases to where direct measurement of individual trees and prediction of their volume,biomass,and carbon are implemented as part of standard inventory protocols.Analyses including the full spectrum of tree sizes show that seedlings contribute roughly 0.6%–0.7%of the total tree volume/mass.This additional suite of information provides opportunities for more holistic assessments across the full spectrum of the tree resource or for specialized subdomains that include the seedling component.
基金funded by the grant“EVA4.0”,No.Z.02.1.01/0.0/0.0/16_019/0000803 supported by OP RDE as well as by the projects APVV-19-0387,APVV-22-0056,and APVV-23-0293 from the Slovak Research and Development Agencyco-funded by the European Commission under the Horizon Europe Teaming for Excellence action+1 种基金project Ligno Silvagrant agreement No.101059552。
文摘This study investigated biomass allocation in young stands of European beech(Fagus sylvatica L.)and Norway spruce(Picea abies(L.)Karst.)across 31 forest sites in the Western Carpathians,Slovakia.A total of 541 trees aged 2–10 years,originating from natural regeneration and planting,were destructively sampled to quantify biomass in four components:foliage,branches,stems,and roots.Generalized non-linear least squares(GNLS)models with a weighing variance function outperformed log-transformed seemingly unrelated regression(SUR)models in terms of accuracy and robustness,especially for foliage and branch biomass.When using height as the predictor,SUR models tended to underestimate biomass in planted beech,leading to notable underprediction of aboveground and total biomass.Biomass allocation patterns varied significantly by species and regeneration origin.Using a non-linear system of equations and component ratio modelling,we found out that planted spruce displayed low variability and a consistent dominance of needle biomass,while naturally regenerated beech showed greater variability and a higher proportion of stem biomass,reflecting stronger competition-driven vertical growth.Interspecific differences in total biomass were more pronounced when using tree height,with spruce generally exhibiting greater biomass than beech at equivalent heights.Overall,stem base diameter marginally outperformed tree height as a predictor of biomass.However,tree height-based models showed strong performance and are particularly suitable for integration with remote sensing applications.These findings can directly support forest managers and modellers in comparing regeneration methods and biomass estimation approaches for early-stage stand development,carbon accounting,and remote sensing calibration.
基金supported by the Xingdian Talent Support Program of Yunnan Province(E5YNR03B01)the Xishuangbanna State Rainforest Talent Support Program(E4BN041B01)the CAS President’s International Fellowship Initiative(2020FYB0003)。
文摘Allometric equations are fundamental tools in ecological research and forestry management,widely used for estimating above-ground biomass and production,serving as the core foundations of dynamic vegetation models.Using global datasets from Tallo(a tree allometry and crown architecture database encompassing thousands of species)and TRY(a plant traits database),we fit B ayesian hierarchical models with three alternative functional forms(powerlaw,generalized Michaelis-Menten(gMM),and Weibull)to characterize how diameter at breast height(DBH),tree height(H),and crown radius(CR)scale with and without wood density as a species-level predictor.Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups,whereas the CR-DBH relationship was best predicted by a power-law function in angiosperms and by the gMM function in gymnosperms.Although including wood density did not significantly improve predictive performance,it revealed important ecological trade-offs:lighter-wood angiosperms achieve taller mature heights more rapidly,and denser wood promotes wider crown expansion across clades.We also found that accurately estimating DBH required considering both height and crown size,highlighting how these variables together distinguish trees of similar height but differing trunk diameters.Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density,though not always predictive at broad scales,helps illuminate the biomechanical and ecological constraints underlying diverse tree architectures.These findings offer practical pathways for integrating height-and crown-based metrics into existing carbon monitoring programs worldwide.
基金supported by the National Natural Science Foundation of China(grant numbers 32471851,32171759 and 32201533)Double Thousand Plan of Jiangxi Province(jxsq2023201058)Jiangxi Province Ganpo Juncai Support Plan(2024BCE50043).
文摘Soil organic carbon in forest affects nutrient availability,microbial processes,and organic matter inputs.Dominant tree species have increasingly shifted from ectomycorrhizal to arbuscular mycorrhizal associations in subtropical forests.However,the consequences of this shift for soil organic carbon is poorly understood.To address this,a field study was conducted across a natural gradient of arbuscular tree associations to investigate how different mycorrhizal associations affect soil organic carbon quantity,composition,chemical stability,and related soil properties.Soil organic carbon fractions,functional groups,microbial enzyme activities were analyzed.Results showed that increasing arbuscular mycorrhizal dominance was associated with declines in total soil organic carbon,particularly in recalcitrant and aromatic carbon forms.Ectomycorrhizaldominated forests exhibited higher nitrogen availability and elevated nitrogen-hydrolyzing enzyme activity,suggesting enhanced nitrogen acquisition strategies that suppress soil organic carbon decomposition and promote carbon retention.These findings indicate that mycorrhizal-mediated shifts in tree composition may significantly alter soil carbon sequestration potential.Incorporating mycorrhizal functional traits into forest management and carbon modeling could improve predictions of soil organic carbon responses under future environmental change.
文摘Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.
基金supported initially by the LIFE FoResMit Project(LIFE14 CCM/IT/000905)。
文摘Peri-urban plantations in the Mediterranean are often degraded due to human inactivity and climate change,leading to a loss of ecosystem services and biodiversity.This study investigates the impact of different thinning practices on carbon sequestration and tree stability in a degraded periurban plantation in the Italian Apennines,six years after thinning.Three treatments were compared:(a)moderate thinning from below(-25%biomass),representing the typical practice;(b)intense selective thinning(-35%biomass),representing an innovative approach;and(c)no management as the control.Growth projections were used to estimate carbon recovery for these treatments,based on site-specific models calibrated with real data.The results show that both thinning approaches increased carbon sequestration over time,with the innovative thinning achieving a 7%higher annual carbon sequestration rate than traditional thinning and 8%more than the control.Estimated payback times were9 years for recovering the harvested volume in both thinning approaches,10 years for innovative thinning to surpass traditional thinning,17 years for innovative thinning to surpass the control,and 24 years for traditional thinning to surpass the control.Additionally,tree mechanical stability improved significantly in both thinning treatments after two years,with further increases observed in the innovative thinning group after six years.These results suggest that selective thinning can accelerate forest recovery and carbon sequestration,especially in areas with high stem density,where it can reduce the negative impacts of tree mortality and deadwood accumulation.However,careful planning is required to mitigate potential short-term stability is sues,particularly in challenging environments(e.g.,windy conditions,steep slopes).Forest management strategies should therefore aim to balance growth,carbon storage,and tree stability,considering both long-term sustainability and local environmental conditions.The findings are particularly relevant for current climate change mitigation strategies,emphasizing that thinning should be carefully tailored to forest type and conditions to maximize benefits in carbon credit generation and sustainable forest management practices.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
基金supported by the Czech University of Life Sciences(Internal Grant Agency:A_12_24,43110/1312/3103,the Czech Science Foundation(Grant GACR No.21-27454S)Technology Agency of the Czech Republic(TACR No.SS06010420)+2 种基金provided by the CLIMB-FOREST project(No.101060554)project FORbEST(No.101181878)funded under the Horizon Europe Framework Programme。
文摘Old-growth forests uniquely support biodiversity while serving as some of the planet's most important carbon stocks.The influence of tree and stand age on carbon flux dynamics remains debated—an urgent question as climate-driven disturbances may reshape forest age structures and in situ carbon storage.To clarify these relationships in Fagus sylvatica,systems,we examined a unique dataset of 3,503 tree ring series from 190 plots across some of the best preserved old-growth forests from five southern European countries.By employing a dendrochronological approach and integrating key environmental variables,including elevation,slope,temperature,and the presence of large-diameter trees(≥60 cm),we analyzed the complex relationships between tree/stand age within a plot(represented by plot-level mean values,hereafter“stand age”)and aboveground carbon stock across live,standing,and lying deadwood pools.The average stand age was 220 years,with 230 tC⋅ha^(-1) of carbon stored in aboveground biomass and necromass.We found a positive correlation between age and carbon storage at both the individual tree and plot levels.Notably,the presence of large-diameter trees was the strongest indicator of carbon stock,with carbon accumulation peaking at about 30%large-tree stems proportion before stabilising,while younger beech trees(below 100 years old)had a smaller contribution to carbon storage.We found no evidence of a decline in carbon stock with advancing stand age across the studied sites.Despite the ecological importance of old-growth forests,many of them remain unprotected and are disappearing across Europe.Our findings highlight the importance of preserving old-growth forests to maximize their role as long-term ecosystem carbon reservoirs.
基金funded by the Science and Technology Program of Gansu Province(25JRRA487)the National Natural Science Foundation of China(42101072)the Key Research and Development Program of Gansu Province(22YF7FA029).
文摘Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to ongoing debate in this field.Moreover,gaps remain in understanding the climatic mechanisms driving increased tree growth variability,particularly for species simultaneously limited by multiple climate factors.In this study,we assessed the temporal trends in variability of Picea purpurea radial growth and its linkage with growth-climate sensitivity utilizing dendrochronological methods.Our results revealed a significant increase in P.purpurea radial growth variability from 1960 to 2020,as indicated by continuous rises in the standard deviation,coefficient of variation,and mean sensitivity of tree-ring width indices.The increased frequency of extreme growth declines further supported this finding.Furthermore,moisture condition in July was identified as a key limiting factor of P.purpurea growth.Notably,the strengthening relationship between tree-ring width indices and vapor pressure deficit(VPD)suggests that the moisture sensitivity for P.purpurea growth has increased over the period 1960-2020.This enhanced sensitivity to VPD,whose interannual variability has also increased synchronously,may have contributed to the rise in P.purpurea growth variability.Additionally,the maximum temperature in May was positively correlated with P.purpurea growth;however,there is little evidence that this factor contributed to the observed increase in growth variability.These findings provide new insights into P.purpurea growth trends and improve our understanding of the potential future impacts of climate change on forest ecosystems.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
文摘We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.
基金supported by the research start-up funds for invited doctor of Lanzhou University of Technology under Grant 14/062402。
文摘In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.
基金funded by the Global PhD Scholarship between KU Leuven and UCLouvain。
文摘Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and diversity on leaf litter decomposition in forests remains understudied.By controlling for macroclimate,soil properties,and litter substrate in a mature common garden,we investigated whether a three-month tea bag incubation of standardized green and rooibos tea substrate is driven by canopy tree species characteristics and diversity.Our study hypothesized two primary pathways:a chemical engineering effect,where trees alter soil properties and decomposer communities through litter input,and a physical engineering effect,where tree canopy structure modulates the local microclimate.The results showed that even under uniform macroclimatic and initial soil conditions,mass loss rates varied widely for green tea(27.4%–73.2%)and rooibos tea(6.1%–34.7%),comparable as found in other research between distinct biomes.While substrate quality was the dominant factor,both engineering pathways and,to a minor extent,tree diversity modulated mass losses.For green tea,tree chemical and physical characteristics seemed equally important,while the physical environment showed an increased importance for rooibos.Incubation depth played a key role,where forest floor decomposition rates are more susceptible to temporal climate variations,and soil-layer decomposition rates are less susceptible to climate variations and more determined by tree species identity.Our findings suggest that tea bag experiments focusing solely on topsoil burial may underestimate processes in the forest floor and the mineralorganic boundary layer.This study underscores the critical role of litter substrate quality in decomposition while demonstrating that tree community composition and the associated herbaceous layer,through both chemical and physical engineering pathways,strongly modulate decomposition rates.
基金supported by the National Natural Science Foundation(32130068,32271634,and 32071597)CAS Key Laboratory of Forest Ecology and Silviculture,Institute of Applied Ecology,Chinese Academy of Sciences(KLFES-2025)。
文摘Urban forests are highly multifunctional and provide numerous ecological functions.Plant functional traits individually or jointly influence the ecological multifunctionality of tree species(TS-EMF)and can also modify TSEMF in response to environmental changes.However,there has been limited exploration of multitrait combinations for predicting TS-EMF across seasons and of trait thresholds that enhance TS-EMF.Here,for 10 dominant tree species in urban forests of Northeast China,14 traits were measured and four aboveground and three belowground ecological functions assessed in three seasons.Ecological functions and TS-EMF differed significantly throughout the seasons(P<0.05).Synergistic relationships were found between carbon sequestration and oxygen release,between cooling and humidification,and between organic carbon accumulation and nutrient cycling.Notably,aboveground multifunctionality played a leading role in TS-EMF.With seasonal changes,resource allocation shifted toward traits related to resource acquisition rather than conservation to maintain TS-EMF.The combination of traits that predicted TS-EMF varied by type,accounting for up to 66.45%of the variation.TS-EMF was primarily driven by leaf structure in spring and by nutrient accumulation in autumn.Leaf carbon content(LCC)consistently served as a stabilizing factor for predicting TS-EMF across seasons.At 36.5-36.8 mg g^(-1),LCC had its optimal effect on TS-EMF.Other traits in combination that positively influence total TS-EMF include leaf nitrogen content(3.43-3.45 mg g^(-1)),leaf phosphorus content(0.80-0.83 mg g^(-1)),and leaf area(65.86-68.43 cm^(2)).Within these specified trait thresholds,Morus alba and Quercus mongolica were identified as key species.These findings suggest that the trade-off between various ecological functions can be managed by altering plant traits across seasons.This approach could provide a theoretical foundation for enhancing the TS-EMF of urban forests through trait-based management,offering practical guidance for selecting tree species.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
文摘Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.
文摘Mood-driven purchases of both goods and services are on the rise,emerging as the new engine driving consumption.MINIATURE decorative green banana trees called“No Anxiety”(a word play on the Chinese words“don’t be a green banana”),blind boxes,mountain hiking companions,and AI chat pals-many products and services that were dismissed as useless or worthless years ago are now gaining popularity among young consumers.Behind this trend is the increase in consumer spending,driven more by emotions than practical needs.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
