Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with ...Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with Class Ⅰ/Ⅱ reservoirs remaining underrepresented due to the difficulties in simulating the geothermal gradient and interlayer interactions.This study investigates depressurization performance across all three classes using a novel 360°rotatable reactor with segmented temperature control,enabling precise simulation of reservoir conditions.Results reveal:(i)Class Ⅰ shows two-stage gas production,with 50%from early free gas enabling rapid depressurization,followed by dissociated gas dominance.They achieve 38.4%-78.3%higher cumulative production and superior gas-to-water ratios due to efficient energy use.(ii)The free gas layer in Class Ⅰ accelerates pressure and heat transfer.Class Ⅱ’s water layer provides sensible heat but causes water blocking,impairing heat flow.Class Ⅲ exhibits rapid initial dissociation but a quick decline without fluid support.(iii)Low temperature,low hydrate saturation,and high production pressure collectively reduce efficiency by increasing flow resistance,limiting gas supply,and reducing dissociation drive.Over-depressurization risks hydrate reformation and ice blockage.This work bridges experimental gaps for Class Ⅰ/Ⅱ reservoirs,offering key insights for optimizing recovery.展开更多
Surface/underwater target classification is a key topic in marine information research.However,the complex underwater environment,coupled with the diversity of target types and their variable characteristics,presents ...Surface/underwater target classification is a key topic in marine information research.However,the complex underwater environment,coupled with the diversity of target types and their variable characteristics,presents significant challenges for classifier design.For shallow-water waveguides with a negative thermocline,a residual neural network(ResNet)model based on the sound field elevation structure is constructed.This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches.Meanwhile,to address the reduced generalization ability caused by limited labeled acoustic data,an improved ResNet model based on unsupervised domain adaptation(“proposed UDA-ResNet”)is further constructed.This model incorporates data on simulated elevation structures of the sound field to augment the training process.Adversarial training is employed to extract domain-invariant features from simulated and trial data.These strategies help reduce the negative impact caused by domain differences.Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes,thus confirming its feasibility and effectiveness.展开更多
Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct...Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.展开更多
In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these chall...In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
Cobalt is undoubtedly the most promising alternative metal to rhodium for a highly active and stable hydroformylation process under mild conditions.In this study,two cobalt-based heterogeneous catalysts were synthesiz...Cobalt is undoubtedly the most promising alternative metal to rhodium for a highly active and stable hydroformylation process under mild conditions.In this study,two cobalt-based heterogeneous catalysts were synthesized via impregnating a cobalt precursor into polymers(POPs-NVP).Comprehensive characterization revealed that the cobalt species on the catalysts exist as CoO with two distinct sizes:nanoparticles and single sites.The CoO nanoparticles on POPs-NVP exhibited outstanding hydroformylation activity(81.7%yield of aldehyde and alcohol,13.5%yield of alkane),while CoO single sites displayed robust olefin hydrogenation performance(62.6%yield of alkane,27.3% yield of aldehyde and alcohol).These divergent catalytic behaviors were attributed to distinct electron density distributions around surface-exposed cobalt species,which were critically governed by CoO sizes on catalysts.By elucidating the size-dependent effects of CoO/POPs-NVP catalysts,this work provided insights into the complex active species states in heterogeneous cobalt-based catalysts,and established valuable experimental and theoretical foundations for designing highly efficient cobalt-based heterogeneous catalysts for hydroformylation.展开更多
Lattice materials have demonstrated promising potential in engineering applications owing to their exceptional lightweight,high specific strength,and tunable mechanical properties.However,the traditional homogenizatio...Lattice materials have demonstrated promising potential in engineering applications owing to their exceptional lightweight,high specific strength,and tunable mechanical properties.However,the traditional homogenization methods based on the classical elasticity theory struggle to accurately describe the non-classical mechanical behaviors of lattice materials,especially when dealing with complex unit-cell geometries featured by non-symmetric configurations or non-single central node connections.In response to this limitation,this study establishes a generalized homogenization model based on the micropolar theory framework,employing Hill's boundary conditions to precisely predict the equivalent moduli of complex lattice materials.By introducing the independent rotational degree of freedom(DOF)characteristic of the micropolar theory,the proposed model successfully overcomes the limitation of conventional methods in accurately describing the asymmetric deformation and scale effects.We initially calculate the constitutive relations of two-dimensional(2D)cross-shaped multi-node chiral lattices and subsequently extend the method to three-dimensional(3D)lattices,successfully predicting the mechanical properties of both traditional and eccentric body-centered cubic(BCC)lattices.The theoretical model is validated through the finite element numerical verification which shows excellent consistency with the theoretical predictions.A further parametric study investigates the influence of geometric parameters,revealing the underlying size-effect mechanism.This paper provides a reliable theoretical tool for the design and property optimization of complex lattice materials.展开更多
The alpine grassland vegetation on the Qinghai-Tibet Plateau is composed of plant patches in varied sizes.It remains uncertain whether vegetation recovery following grazing exclusion(GE)in degraded grasslands is drive...The alpine grassland vegetation on the Qinghai-Tibet Plateau is composed of plant patches in varied sizes.It remains uncertain whether vegetation recovery following grazing exclusion(GE)in degraded grasslands is driven by increases in patches number(NP),patch size(PS),or both.We based our predictions on two hypotheses:GE intensifies plant competition,and facilitation prevails near patches while competition prevails in interpatch spaces.We predicted that the NP would remain stable or decrease and PS would increase under GE treatment.To evaluate these predictions,we conducted a study in six lightly degraded alpine grasslands under free grazing(FG)conditions in Bangor County,Xizang Autonomous Region,China,with corresponding GE treatments using transects in 2017 and 2018.Results revealed that four sites in 2017 and five sites in 2018 had reduced NP and increased PS,with probabilities of 0.033(2017)and 0.004(2018),respectively,and a joint probability of 0.0001 under the null hypothesis that GE does not affect NP or PS.The NP reduction was solely due to the decrease in small patch sizes.An increase in PS was common across species,and a predominant tendency for NP reduction was observed among species across the sites.The overall changes in NP and PS were primarily driven by the three most abundant species(contributing more than 60%in both years),rather than by shifts in floristic composition.Our findings highlight that vegetation recovery in Bangor alpine steppes following GE relies solely on the expansion of existing patches rather than the recruitment of new ones in interpatch gaps.We recommend prioritizing growth-promoting measures,such as nutrient or water management,over seed addition when assisting with GE for restoring lightly degraded grasslands.展开更多
Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study invest...Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.展开更多
High-performance magnesium alloys are in great demand to meet the lightweight design requirements of aircraft.Grain size has long been recognized as a key factor influencing the mechanical properties of alloys.This st...High-performance magnesium alloys are in great demand to meet the lightweight design requirements of aircraft.Grain size has long been recognized as a key factor influencing the mechanical properties of alloys.This study investigates the effect of grain size,controlled by Zr addition,on the fatigue behavior of a recently developed low-cost Mg-2.6Nd-0.35Zn alloy,through systematic characterization and analysis of stress-life(S-N)curves,fatigue crack propagation,fracture surface morphology,stress intensity factor,and crack propagation threshold.The results show that after heat treatment(solution at 525±5℃ for 8 h and water quenching at 60-80℃,followed by aging at 250±5℃for 14 h and then air cooling),coarse-grained specimens(average grain size approximately 596μm)containing 0.12wt.%Zr exhibit greater resistance to fatigue crack propagation than fine-grained specimens(average grain size approximately 94μm)containing 0.46wt.%Zr.Coarse grains promote intergranular fracture,while fine grains favor transgranular fracture.In addition,coarse grains reduce the sensitivity of the crack tip to stress concentration.Furthermore,fine-grained samples demonstrate a longer total fatigue life,owing to their superior resistance to crack initiation,which significantly prolongs the crack initiation stage.These findings highlight the importance of optimizing grain size to achieve the best possible fatigue resistance in Mg-Nd-Zn-Zr alloys for practical engineering applications.展开更多
Riparian dunes in deserts exhibit unique geographic features due to aeolian-fluvial interactions.In this study,we collected 510 surface sediment samples from eight drainage basins and conducted a systematic analysis t...Riparian dunes in deserts exhibit unique geographic features due to aeolian-fluvial interactions.In this study,we collected 510 surface sediment samples from eight drainage basins and conducted a systematic analysis to examine the grain size characteristics of major riparian dunes in the typical cold and arid deserts of China.The results indicate that major riparian dunes of deserts in study area can be classified into three types based on their grain size characteristics.The Bartlett test of sphericity and the Kaiser-Meyer-Olkin(KMO)test were also performed,and their significance values were found to be 0.000 and 0.584,respectively.The results of the principal component analysis revealed that the cumulative contribution rate of the total variance reached 85.9%for the two principal components with characteristic roots greater than 1.0.The primary principal component included medium sand,whereas the secondary principal component included fine sand.We conducted a cluster analysis and classified the samples into three major types.Type I rivers include the Keriya River,Langqu River,Tora River and Heihe River,which are characterized by by fine particle size,and well-sorted.Type II includes Mu Bulag River,Kuye River,and the Xar Moron River,Compared with type I,it has a relatively coarser mean grain size and relatively poor sorting for this type.Type III includes the Maquan River,which is characterized mainly by fine sand and medium sand,accounting for more than 90%,and the sorting coefficient(0.52)suggests relatively well sorting in this pattern.Moreover,principal component analysis was applied to determine the particle sizes of samples from different watersheds.Moreover,these sediments exhibit both hydromorphic and aeolian features.At the drainage basin scale,the mode and intensity of aeolian-fluvial interactions depend on climatic conditions.In arid and semi-arid climate regions,wind is the dominant force,and the grain size exhibits significant aeolian features.Conversely,in the semi-humid region,flowing water is the dominant force,and riparian dunes in this region are formed by aeolian-fluvial interaction.The angle between the wind direction and flow direction in different reaches influences both the supply of sediment sources and the development of riparian dunes.This study will provide a new perspective for evaluating aeolian-fluvial interactions on riparian dunes in the deserts of China’s cold and arid regions.展开更多
Landslide dams often undergo seepage due to poor particle gradation and loose structure,yet most existing studies focus solely on overtopping-induced breaching mechanisms,neglecting the potential influence of pre-brea...Landslide dams often undergo seepage due to poor particle gradation and loose structure,yet most existing studies focus solely on overtopping-induced breaching mechanisms,neglecting the potential influence of pre-breaching seepage.Seepage may alter the dam's erodibility,structural stability,and material composition,thereby affecting the overtopping breaching process.Through flume experiments,this study investigates the breaching mechanisms of cohesionless landslide dams with different gradations within the same particle size range under coupled seepage-overtopping conditions.The results demonstrate that pre-breaching seepage significantly impacts breaching dynamics.Within a specific particle size range,compared to pure overtopping,seepage reduces downstream slope stability,increases material erodibility,shortens breaching duration,amplifies peak discharge,and advances the timing of peak flow.As the median particle size(D_(50))increases,the amplification effect of seepage on peak discharge initially increases then decreases,the advancement of peak flow timing diminishes,and the breach erosion rate declines.When D_(50)is sufficiently large,seepage has negligible effects on breach development.For smaller D_(50),seepage markedly accelerates breach widening and deepening.Furthermore,coupled seepage-overtopping extends the downstream deposition area and exacerbates channel erosion due to differences in sediment sorting.These findings highlight the critical role of seepage in landslide dam breaching,providing a scientific basis for hazard prevention and mitigation.展开更多
With the development of the semiconductor industry below the 7 nm scale,critical dimension small-angle X-ray scattering(CD-SAXS)has emerged as a powerful tool for quantitatively measuring nanoscale deviations.In this ...With the development of the semiconductor industry below the 7 nm scale,critical dimension small-angle X-ray scattering(CD-SAXS)has emerged as a powerful tool for quantitatively measuring nanoscale deviations.In this study,the effects of X-ray beam size and photon energy on the accuracy of critical dimension measurements were investigated.Critical dimensions measured using beams with different spot sizes showed different deviations from the expected values.Beam sizes that were either too large or too small did not improve confidence intervals.As the incident energy increased,the X-ray transmission rate increased,while the scattering cross section decreased,resulting in a gradual decrease in the signal-to-noise ratio of the diffraction peaks,which reduced the accuracy of the CD-SAXS measurements.An optimal accuracy was obtained at 12 keV with a smaller beam size.Using an effective trapezoid model,the results yielded an average pitch of 100.4±0.2 nm,width of 49.8±0.2 nm,height of 130.0±0.2 nm,and a sidewall angle below 1.1°±0.1°.These results provide crucial guidance for the future development of CD-SAXS laboratories and the construction of X-ray machines as well as robust support for research in related fields.展开更多
The specific surface area(S S)and pore size(D)exhibit an inherent trade-off in the microscale design of bone implants:larger pores typically correlate with reduced surface area and vice versa.This relationship has att...The specific surface area(S S)and pore size(D)exhibit an inherent trade-off in the microscale design of bone implants:larger pores typically correlate with reduced surface area and vice versa.This relationship has attracted notable attention because of its critical role in the regulation of cell adhesion and osteogenesis.However,it remains largely unclear how S S and D affect the generated bone tissue and dynamically change during long-term osteogenesis.Herein,by applying rigorous geometric mapping to minimal surfaces,we constructed precisely partitioned and layer-by-layer thickened tissue models to simulate osteogenesis across different temporal scales and thereby track the dynamic evolution of geometric characteristics,permeability,and mechanobiological tissue differentiation.The high-S S samples were found to facilitate the rapid formation of new bone tissue in the early stages.However,their smaller pores tended to cause occlusions,hindering further tissue development.In contrast,low-S S samples showed slower bone regeneration,but their larger pores provided adequate physical space for tissue regeneration and mass transport,ultimately promoting bone formation in the long term.Mechanobiological regulation suggests that fibrous tissue formation inhibits additional bone formation,establishing a dynamic equilibrium between osteogenesis and pore space to sustain nutrient/waste exchange throughout the regenerative process.Overall,smaller pores are preferable in implants for minimally loaded osteoplasty procedures focused on early-stage bone consolidation,whereas larger pores are preferable in dynamically loaded implants requiring prolonged mechanical stability.展开更多
Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces th...Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.展开更多
Bacterial cells are widely accepted as nucleation sites for calcium carbonate precipitation in biomineralization based on the Microbially Induced Carbonate Precipitation(MICP)process.For MICP-based insitu biotreatment...Bacterial cells are widely accepted as nucleation sites for calcium carbonate precipitation in biomineralization based on the Microbially Induced Carbonate Precipitation(MICP)process.For MICP-based insitu biotreatment,the firstproblem to be solved is how to introduce and retain the bacterial cells in the soil,which involves the migration and retention of bacterial cells during the biogrouting process.Soil particle size,a key factor in determining pore throat size,can have a significanteffect on the migration and retention of bacterial cells in the soil and therefore on biomineralization.To investigate the effect of particle size on the migration and retention of bacterial cells in sand and its biomineralization,two sets of tests were carried out in this study,including percolation tests and sand column treatment tests.Soil urease activity(definedas urease activity per unit mass of soil)and calcium carbonate content of the biomineralized sand were measured to comprehensively assess the migration and retention of bacterial cells in the sand.The results indicate that sands with a particle size smaller than 0.25 mmwould inhibit the migration of bacteria in the sand,resulting in a nonuniform distribution of precipitated calcium carbonate and a low strength enhancement of biomineralization.On the other hand,sands with a particle size larger than 1.18 mm are unfavorable for retaining bacterial cells in the sand,resulting in low calcium conversion efficiency.Meanwhile,particle size would also affect the formation of effective calcium carbonate through interparticle contact number and interparticle pore size,and thus biomineralization.展开更多
基金partially funded by Shenzhen Science and Technology Program(No.JCYJ20240813112038050)the National Natural Science Foundation of China(No.52404059)+1 种基金the Economy Trade and Information Commission of Shenzhen Municipality,China(No.HYCYPT20140507010002)the Key Program of Marine Economy Development(Six Marine Industries)Special Foundation of the Department of Natural Resources of Guangdong Province,China(No.GDOE[2021]55).
文摘Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with Class Ⅰ/Ⅱ reservoirs remaining underrepresented due to the difficulties in simulating the geothermal gradient and interlayer interactions.This study investigates depressurization performance across all three classes using a novel 360°rotatable reactor with segmented temperature control,enabling precise simulation of reservoir conditions.Results reveal:(i)Class Ⅰ shows two-stage gas production,with 50%from early free gas enabling rapid depressurization,followed by dissociated gas dominance.They achieve 38.4%-78.3%higher cumulative production and superior gas-to-water ratios due to efficient energy use.(ii)The free gas layer in Class Ⅰ accelerates pressure and heat transfer.Class Ⅱ’s water layer provides sensible heat but causes water blocking,impairing heat flow.Class Ⅲ exhibits rapid initial dissociation but a quick decline without fluid support.(iii)Low temperature,low hydrate saturation,and high production pressure collectively reduce efficiency by increasing flow resistance,limiting gas supply,and reducing dissociation drive.Over-depressurization risks hydrate reformation and ice blockage.This work bridges experimental gaps for Class Ⅰ/Ⅱ reservoirs,offering key insights for optimizing recovery.
基金supported by the National Natural Science Foundation of China(Grant Nos.62471024 and 62301183)the Open Research Fund of Hanjiang Laboratory(KF2024001).
文摘Surface/underwater target classification is a key topic in marine information research.However,the complex underwater environment,coupled with the diversity of target types and their variable characteristics,presents significant challenges for classifier design.For shallow-water waveguides with a negative thermocline,a residual neural network(ResNet)model based on the sound field elevation structure is constructed.This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches.Meanwhile,to address the reduced generalization ability caused by limited labeled acoustic data,an improved ResNet model based on unsupervised domain adaptation(“proposed UDA-ResNet”)is further constructed.This model incorporates data on simulated elevation structures of the sound field to augment the training process.Adversarial training is employed to extract domain-invariant features from simulated and trial data.These strategies help reduce the negative impact caused by domain differences.Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes,thus confirming its feasibility and effectiveness.
文摘Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.
基金supported by the European Research Council(ERC)under Grant Agreement No.951424(Water-Futures)by the Republic of Cyprus through the Deputy Ministry of Research,Innovation and Digital Policy.
文摘In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金supported by the National Key Research and Development Program of China(2023YFA1508003)the National Natural Science Foundation of China(22408363,22302192)+6 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA29050300)the Youth Innovation Promotion Association CAS(2021181)the Key Research and Development Program of Liaoning(2023JH2/101800051)the Dalian of Science and Technology Project(2023RY012)the Postdoctoral Fellowship Program of CPSF(GZC20241677,GZB20230724)the Postdoctoral Science Foundation(2024T170900)the Doctoral Research Start-up Fund of Liaoning(2024-BSBA-28)。
文摘Cobalt is undoubtedly the most promising alternative metal to rhodium for a highly active and stable hydroformylation process under mild conditions.In this study,two cobalt-based heterogeneous catalysts were synthesized via impregnating a cobalt precursor into polymers(POPs-NVP).Comprehensive characterization revealed that the cobalt species on the catalysts exist as CoO with two distinct sizes:nanoparticles and single sites.The CoO nanoparticles on POPs-NVP exhibited outstanding hydroformylation activity(81.7%yield of aldehyde and alcohol,13.5%yield of alkane),while CoO single sites displayed robust olefin hydrogenation performance(62.6%yield of alkane,27.3% yield of aldehyde and alcohol).These divergent catalytic behaviors were attributed to distinct electron density distributions around surface-exposed cobalt species,which were critically governed by CoO sizes on catalysts.By elucidating the size-dependent effects of CoO/POPs-NVP catalysts,this work provided insights into the complex active species states in heterogeneous cobalt-based catalysts,and established valuable experimental and theoretical foundations for designing highly efficient cobalt-based heterogeneous catalysts for hydroformylation.
基金Project supported by the National Natural Science Foundation of China(No.12472077)the supports from Shanghai Gaofeng Project for University Academic Program Development,Fundamental Research Funds for the Central Universities(No.22120240353).
文摘Lattice materials have demonstrated promising potential in engineering applications owing to their exceptional lightweight,high specific strength,and tunable mechanical properties.However,the traditional homogenization methods based on the classical elasticity theory struggle to accurately describe the non-classical mechanical behaviors of lattice materials,especially when dealing with complex unit-cell geometries featured by non-symmetric configurations or non-single central node connections.In response to this limitation,this study establishes a generalized homogenization model based on the micropolar theory framework,employing Hill's boundary conditions to precisely predict the equivalent moduli of complex lattice materials.By introducing the independent rotational degree of freedom(DOF)characteristic of the micropolar theory,the proposed model successfully overcomes the limitation of conventional methods in accurately describing the asymmetric deformation and scale effects.We initially calculate the constitutive relations of two-dimensional(2D)cross-shaped multi-node chiral lattices and subsequently extend the method to three-dimensional(3D)lattices,successfully predicting the mechanical properties of both traditional and eccentric body-centered cubic(BCC)lattices.The theoretical model is validated through the finite element numerical verification which shows excellent consistency with the theoretical predictions.A further parametric study investigates the influence of geometric parameters,revealing the underlying size-effect mechanism.This paper provides a reliable theoretical tool for the design and property optimization of complex lattice materials.
基金financially supported by the Science and Technology Bureau of Ali Prefecture,project named“Assessing the Carbon Sequestration and Carbon Sink Enhancement Potential of Natural Ecosystems in Ali Region(QYXTZX-AL2022-05)”。
文摘The alpine grassland vegetation on the Qinghai-Tibet Plateau is composed of plant patches in varied sizes.It remains uncertain whether vegetation recovery following grazing exclusion(GE)in degraded grasslands is driven by increases in patches number(NP),patch size(PS),or both.We based our predictions on two hypotheses:GE intensifies plant competition,and facilitation prevails near patches while competition prevails in interpatch spaces.We predicted that the NP would remain stable or decrease and PS would increase under GE treatment.To evaluate these predictions,we conducted a study in six lightly degraded alpine grasslands under free grazing(FG)conditions in Bangor County,Xizang Autonomous Region,China,with corresponding GE treatments using transects in 2017 and 2018.Results revealed that four sites in 2017 and five sites in 2018 had reduced NP and increased PS,with probabilities of 0.033(2017)and 0.004(2018),respectively,and a joint probability of 0.0001 under the null hypothesis that GE does not affect NP or PS.The NP reduction was solely due to the decrease in small patch sizes.An increase in PS was common across species,and a predominant tendency for NP reduction was observed among species across the sites.The overall changes in NP and PS were primarily driven by the three most abundant species(contributing more than 60%in both years),rather than by shifts in floristic composition.Our findings highlight that vegetation recovery in Bangor alpine steppes following GE relies solely on the expansion of existing patches rather than the recruitment of new ones in interpatch gaps.We recommend prioritizing growth-promoting measures,such as nutrient or water management,over seed addition when assisting with GE for restoring lightly degraded grasslands.
基金funded by the Natural Science Foundation of China(Grants No 42277127)。
文摘Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.
文摘High-performance magnesium alloys are in great demand to meet the lightweight design requirements of aircraft.Grain size has long been recognized as a key factor influencing the mechanical properties of alloys.This study investigates the effect of grain size,controlled by Zr addition,on the fatigue behavior of a recently developed low-cost Mg-2.6Nd-0.35Zn alloy,through systematic characterization and analysis of stress-life(S-N)curves,fatigue crack propagation,fracture surface morphology,stress intensity factor,and crack propagation threshold.The results show that after heat treatment(solution at 525±5℃ for 8 h and water quenching at 60-80℃,followed by aging at 250±5℃for 14 h and then air cooling),coarse-grained specimens(average grain size approximately 596μm)containing 0.12wt.%Zr exhibit greater resistance to fatigue crack propagation than fine-grained specimens(average grain size approximately 94μm)containing 0.46wt.%Zr.Coarse grains promote intergranular fracture,while fine grains favor transgranular fracture.In addition,coarse grains reduce the sensitivity of the crack tip to stress concentration.Furthermore,fine-grained samples demonstrate a longer total fatigue life,owing to their superior resistance to crack initiation,which significantly prolongs the crack initiation stage.These findings highlight the importance of optimizing grain size to achieve the best possible fatigue resistance in Mg-Nd-Zn-Zr alloys for practical engineering applications.
基金Under the auspices of the General Project of Science and Technology Department of Shaanxi Province(No.2023-JCYB-264)General Program of National Natural Science Foundation of China(No.41801004,42371008,42471012)。
文摘Riparian dunes in deserts exhibit unique geographic features due to aeolian-fluvial interactions.In this study,we collected 510 surface sediment samples from eight drainage basins and conducted a systematic analysis to examine the grain size characteristics of major riparian dunes in the typical cold and arid deserts of China.The results indicate that major riparian dunes of deserts in study area can be classified into three types based on their grain size characteristics.The Bartlett test of sphericity and the Kaiser-Meyer-Olkin(KMO)test were also performed,and their significance values were found to be 0.000 and 0.584,respectively.The results of the principal component analysis revealed that the cumulative contribution rate of the total variance reached 85.9%for the two principal components with characteristic roots greater than 1.0.The primary principal component included medium sand,whereas the secondary principal component included fine sand.We conducted a cluster analysis and classified the samples into three major types.Type I rivers include the Keriya River,Langqu River,Tora River and Heihe River,which are characterized by by fine particle size,and well-sorted.Type II includes Mu Bulag River,Kuye River,and the Xar Moron River,Compared with type I,it has a relatively coarser mean grain size and relatively poor sorting for this type.Type III includes the Maquan River,which is characterized mainly by fine sand and medium sand,accounting for more than 90%,and the sorting coefficient(0.52)suggests relatively well sorting in this pattern.Moreover,principal component analysis was applied to determine the particle sizes of samples from different watersheds.Moreover,these sediments exhibit both hydromorphic and aeolian features.At the drainage basin scale,the mode and intensity of aeolian-fluvial interactions depend on climatic conditions.In arid and semi-arid climate regions,wind is the dominant force,and the grain size exhibits significant aeolian features.Conversely,in the semi-humid region,flowing water is the dominant force,and riparian dunes in this region are formed by aeolian-fluvial interaction.The angle between the wind direction and flow direction in different reaches influences both the supply of sediment sources and the development of riparian dunes.This study will provide a new perspective for evaluating aeolian-fluvial interactions on riparian dunes in the deserts of China’s cold and arid regions.
基金support of the National Natural Science Foundation of China(42107189,U20A20111)。
文摘Landslide dams often undergo seepage due to poor particle gradation and loose structure,yet most existing studies focus solely on overtopping-induced breaching mechanisms,neglecting the potential influence of pre-breaching seepage.Seepage may alter the dam's erodibility,structural stability,and material composition,thereby affecting the overtopping breaching process.Through flume experiments,this study investigates the breaching mechanisms of cohesionless landslide dams with different gradations within the same particle size range under coupled seepage-overtopping conditions.The results demonstrate that pre-breaching seepage significantly impacts breaching dynamics.Within a specific particle size range,compared to pure overtopping,seepage reduces downstream slope stability,increases material erodibility,shortens breaching duration,amplifies peak discharge,and advances the timing of peak flow.As the median particle size(D_(50))increases,the amplification effect of seepage on peak discharge initially increases then decreases,the advancement of peak flow timing diminishes,and the breach erosion rate declines.When D_(50)is sufficiently large,seepage has negligible effects on breach development.For smaller D_(50),seepage markedly accelerates breach widening and deepening.Furthermore,coupled seepage-overtopping extends the downstream deposition area and exacerbates channel erosion due to differences in sediment sorting.These findings highlight the critical role of seepage in landslide dam breaching,providing a scientific basis for hazard prevention and mitigation.
基金supported by the National Natural Science Foundation of China(No.12175295)the National Key R&D Program of China(2021YFA1601000)the Shanghai Municipal Science and Technology Major Project。
文摘With the development of the semiconductor industry below the 7 nm scale,critical dimension small-angle X-ray scattering(CD-SAXS)has emerged as a powerful tool for quantitatively measuring nanoscale deviations.In this study,the effects of X-ray beam size and photon energy on the accuracy of critical dimension measurements were investigated.Critical dimensions measured using beams with different spot sizes showed different deviations from the expected values.Beam sizes that were either too large or too small did not improve confidence intervals.As the incident energy increased,the X-ray transmission rate increased,while the scattering cross section decreased,resulting in a gradual decrease in the signal-to-noise ratio of the diffraction peaks,which reduced the accuracy of the CD-SAXS measurements.An optimal accuracy was obtained at 12 keV with a smaller beam size.Using an effective trapezoid model,the results yielded an average pitch of 100.4±0.2 nm,width of 49.8±0.2 nm,height of 130.0±0.2 nm,and a sidewall angle below 1.1°±0.1°.These results provide crucial guidance for the future development of CD-SAXS laboratories and the construction of X-ray machines as well as robust support for research in related fields.
基金financial support from the National Natural Science Foundation of China(No.52035012)the Guangdong Basic and Applied Basic Research Foundation(No.2025A1515012203)。
文摘The specific surface area(S S)and pore size(D)exhibit an inherent trade-off in the microscale design of bone implants:larger pores typically correlate with reduced surface area and vice versa.This relationship has attracted notable attention because of its critical role in the regulation of cell adhesion and osteogenesis.However,it remains largely unclear how S S and D affect the generated bone tissue and dynamically change during long-term osteogenesis.Herein,by applying rigorous geometric mapping to minimal surfaces,we constructed precisely partitioned and layer-by-layer thickened tissue models to simulate osteogenesis across different temporal scales and thereby track the dynamic evolution of geometric characteristics,permeability,and mechanobiological tissue differentiation.The high-S S samples were found to facilitate the rapid formation of new bone tissue in the early stages.However,their smaller pores tended to cause occlusions,hindering further tissue development.In contrast,low-S S samples showed slower bone regeneration,but their larger pores provided adequate physical space for tissue regeneration and mass transport,ultimately promoting bone formation in the long term.Mechanobiological regulation suggests that fibrous tissue formation inhibits additional bone formation,establishing a dynamic equilibrium between osteogenesis and pore space to sustain nutrient/waste exchange throughout the regenerative process.Overall,smaller pores are preferable in implants for minimally loaded osteoplasty procedures focused on early-stage bone consolidation,whereas larger pores are preferable in dynamically loaded implants requiring prolonged mechanical stability.
基金Project supported by the Project of the Anhui Provincial Natural Science Foundation(Grant No.2308085MA19)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0410401)+2 种基金the National Natural Science Foundation of China(Grant No.52202120)the National Key Research and Development Program of China(Grant No.2023YFA1609800)USTC Research Funds of the Double First-Class Initiative(Grant No.YD2310002013)。
文摘Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.
基金support by the National Natural Science Foundation of China(NSFC)(Grant Nos.52178319,42477160,52338007).
文摘Bacterial cells are widely accepted as nucleation sites for calcium carbonate precipitation in biomineralization based on the Microbially Induced Carbonate Precipitation(MICP)process.For MICP-based insitu biotreatment,the firstproblem to be solved is how to introduce and retain the bacterial cells in the soil,which involves the migration and retention of bacterial cells during the biogrouting process.Soil particle size,a key factor in determining pore throat size,can have a significanteffect on the migration and retention of bacterial cells in the soil and therefore on biomineralization.To investigate the effect of particle size on the migration and retention of bacterial cells in sand and its biomineralization,two sets of tests were carried out in this study,including percolation tests and sand column treatment tests.Soil urease activity(definedas urease activity per unit mass of soil)and calcium carbonate content of the biomineralized sand were measured to comprehensively assess the migration and retention of bacterial cells in the sand.The results indicate that sands with a particle size smaller than 0.25 mmwould inhibit the migration of bacteria in the sand,resulting in a nonuniform distribution of precipitated calcium carbonate and a low strength enhancement of biomineralization.On the other hand,sands with a particle size larger than 1.18 mm are unfavorable for retaining bacterial cells in the sand,resulting in low calcium conversion efficiency.Meanwhile,particle size would also affect the formation of effective calcium carbonate through interparticle contact number and interparticle pore size,and thus biomineralization.
