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
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
This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability...This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability.To draw as many general conclusions as possible,the confounding effect of class size is analyzed on 127 C＋＋ systems and 113 Java systems.For each OO metric,the indirect effect that represents the distortion of the association caused by class size and its variance for individual systems is first computed.Then,a statistical meta-analysis technique is used to compute the average indirect effect over all the systems and to determine if it is significantly different from zero.The experimental results show that the confounding effects of class size on the associations between OO metrics and maintainability generally exist,regardless of whatever size metric is used.Therefore,empirical studies validating OO metrics on maintainability should consider class size as a confounding variable.展开更多
A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorp...A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorption coefficient. The modified model performed well using in-situ data from the northern South China Sea, and the results were reliable and accurate. The relative errors of the size-fractioned chlorophyll-a concentration for each size class were: micro-:21%, nano-:41%, pico-:26%, and nano+pico:23%. The model was then applied on ocean color remote sensing data to examine the distribution and variation of phytoplankton size classes in northern South China Sea on a large scale.展开更多
An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitor...An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitoring the health of our oceans, and improving our understanding of the bio-geochemical interactions between phytoplankton and their environment. In this paper a new algorithm is developed for detecting three dominant phytoplankton size classes based on distinct differences in their optical signatures. The technique is validated against an independent coupled satellite reflectance and in situ pigment dataset and run on the 10-year NASA Sea viewing Wide Field of view Sensor （SeaWiFS） data series. Results indicate that on average 3.6% of the global oceanic surface layer is dominated by microplankton, 18.0% by nanoplankton and 78.4% by pieoplankton. Results, however, are seen to vary depending on season and ocean basin.展开更多
Based on the theory of constructivism, this article analyzes the open problems colloquial English large size class teaching of higher vocational public English and points out the countermeasures of colloquial English ...Based on the theory of constructivism, this article analyzes the open problems colloquial English large size class teaching of higher vocational public English and points out the countermeasures of colloquial English large-scale class teaching of higher vocational public English.展开更多
Let G be a finite group and π be a set of primes including at least two elements. We write cd（G） and cs（G） to denote the set of complex irreducible character degrees and conjugacy class sizes of G , respectively,...Let G be a finite group and π be a set of primes including at least two elements. We write cd（G） and cs（G） to denote the set of complex irreducible character degrees and conjugacy class sizes of G , respectively, and write π（m）to denote the set of all prime divisors of a positive integer m . For any 1≠m∈cd（G） and 1≠m∈cs（G）, in this note, we shall present the corresponding group structures of finite group G in the case π（m）=π , respectively, which generalizes the result of finite groups with character degrees of two distinct primes. Furthermore, we shall see that the influence of the two sets on the group structure is analogous.展开更多
Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were deve...Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were developed based on similar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSCStd and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and paleo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.展开更多
Traditional classification algorithms perform not very well on imbalanced data sets and small sample size. To deal with the problem, a novel method is proposed to change the class distribution through adding virtual s...Traditional classification algorithms perform not very well on imbalanced data sets and small sample size. To deal with the problem, a novel method is proposed to change the class distribution through adding virtual samples, which are generated by the windowed regression over-sampling (WRO) method. The proposed method WRO not only reflects the additive effects but also reflects the multiplicative effect between samples. A comparative study between the proposed method and other over-sampling methods such as synthetic minority over-sampling technique (SMOTE) and borderline over-sampling (BOS) on UCI datasets and Fourier transform infrared spectroscopy (FTIR) data set is provided. Experimental results show that the WRO method can achieve better performance than other methods.展开更多
“Four classes of enterprises above designated size”(hereinafter called four-classes enterprises)refer to objects of statistical survey that have reached a certain scale in China’s current statistical method system,...“Four classes of enterprises above designated size”(hereinafter called four-classes enterprises)refer to objects of statistical survey that have reached a certain scale in China’s current statistical method system,including four classes in national economy,namely,industrial enterprises above designated size,construction and real estate development and management enterprises above qualifications,wholesale and retail,catering and accommodation enterprises,and service enterprises above designated size,which are the primary part of national economic and social development activities.This paper is focused on analyzing the practice and difficulties in the current statistics work of four-classes enterprises,and then this paper proposes some recommendations.展开更多
The characteristics of human figures, variable laws and the basic sizes of each part of human body have been found by means of anthropomctric measurements of the middle-arid old-aged men and data analyses. Also, propo...The characteristics of human figures, variable laws and the basic sizes of each part of human body have been found by means of anthropomctric measurements of the middle-arid old-aged men and data analyses. Also, proposals of how to classify dimensions and sizes of the medium human figures among the middle-and old-aged men have been put forward.展开更多
The scratching mechanism of polycrystallineγ-TiAl alloy was investigated at the atomic scale using the molecular dynamics method,with a focus on the influence of different grain sizes.The analysis encompassed tribolo...The scratching mechanism of polycrystallineγ-TiAl alloy was investigated at the atomic scale using the molecular dynamics method,with a focus on the influence of different grain sizes.The analysis encompassed tribological characteristics,scratch morphology,subsurface defect distribution,temperature variations,and stress states during the scratching process.The findings indicate that the scratch force,number of recovered atoms,and pile-up height exhibit abrupt changes when the critical size is 9.41 nm due to the influence of the inverse Hall-Petch effect.Variations in the number of grain boundaries and randomness of grain orientation result in different accumulation patterns on the scratch surface.Notably,single crystal materials and those with 3.73 nm in grain size display more regular surface morphology.Furthermore,smaller grain size leads to an increase in average coefficient of friction,removed atoms number,and wear rate.While it also causes higher temperatures with a larger range of distributions.Due to the barrier effect of grain boundaries,smaller grains exhibit reduced microscopic defects.Additionally,average von Mises stress and hydrostatic compressive stress at the indenter tip decrease as grain size decreases owing to grain boundary obstruction.展开更多
Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been wide...Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.展开更多
As the main geomaterials for coral reefs oil or gas extraction and underground infrastructure construction,coral reef limestone demonstrates significantly distinct mechanical responses compared to terrigenous rocks.To...As the main geomaterials for coral reefs oil or gas extraction and underground infrastructure construction,coral reef limestone demonstrates significantly distinct mechanical responses compared to terrigenous rocks.To investigate the mechanical behaviour of coral reef limestone under the coupling impact of size and strain rate,the uniaxial compression tests were conducted on reef limestone samples with length-to-diameter(L/D)ratio ranging from 0.5 to 2.0 at strain rate ranging from 10^(−5)·s^(−1)to 10^(−2)·s^(−1).It is revealed that the uniaxial compressive strength(UCS)and residual compressive strength(RCS)of coral reef limestone exhibits a decreasing trend with L/D ratio increasing.The dynamic increase factor(DIF)of UCS is linearly correlated with the logarithm of strain rate,while increasing the L/D ratio further enhances the DIF.The elastic modulus increases with strain rate or L/D ratio increasing,whereas the Poisson’s ratio approximates to a constant value of 0.24.The failure strain increases with strain rate increasing or L/D ratio decreasing,while the increase in L/D ratio will inhibit the enhancing effect of the strain rate.The high porosity and low mineral strength are the primary factors contributing to a high RCS of 16.7%–64.9%of UCS,a lower brittleness index and multiple irregular fracture planes.The failure pattern of coral reef limestone transits from the shear-dominated to the splitting-dominated failure with strain rate increasing or L/D ratio decreasing,which is mainly governed by the constrained zones induced by end friction and the strain rate-dependent crack propagation.Moreover,a predictive formula incorporating coupling effect of size and strain rate for the UCS of reef limestone was established and verified to effectively capture the trend of UCS.展开更多
Understanding the fracture behavior of rocks subjected to temperature and accounting for the rock's texture is vital for safe and efficient design.Prior studies have often focused on isolated aspects of rock fract...Understanding the fracture behavior of rocks subjected to temperature and accounting for the rock's texture is vital for safe and efficient design.Prior studies have often focused on isolated aspects of rock fracture behavior,neglecting the combined influence of grain size and temperature on fracture behavior.This study employs specimens based on the particle flow code-grain based model to scrutinize the influence of temperature and grain size discrepancies on the fracture characteristics of sandstone.In pursuit of this goal,we manufactured ninety-six semi-circular bend specimens with grain sizes spanning from 0.5 mm to 1.5 mm,predicated on the mineral composition of sandstone.Recognizing the significance of intra-granular and inter-granular fractures,the grains were considered deformable and susceptible to breakage.The numerical model was calibrated using the results of uniaxial compressive strength(UCS)and Brazilian tests.We implemented thermo-mechanical coupled analysis to simulate mode Ⅰ,mode Ⅱ,and mixed mode(Ⅰ-Ⅱ)fracture toughness tests and subsequently studied alterations in the fracture behavior of sandstone at temperatures from 25℃ to 700℃.Our findings revealed increased fracture toughness as the temperature escalated from 25℃ to 200℃.However,beyond the threshold of 200℃,we noted a decline in fracture toughness.More specifically,the drop in mode Ⅰ fracture toughness was more pronounced in specimens with finer grains than those with coarser grains.Contrarily,the trend was reversed for mode Ⅱ fracture toughness.In contrast,the reduction of mixed mode(Ⅰ-Ⅱ)fracture toughness seemed almost linear across all grain sizes.Furthermore,we identified a correlation between temperature and grain size and their collective impact on crack propagation patterns.Comparing our results with established theoretical benchmarks,we confirmed that both temperature and grain size variations influence the fracture envelopes of sandstone.展开更多
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.
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The National Natural Science Foundation of China(No.60425206,60633010)
文摘This paper uses three size metrics,which are collectable during the design phase,to analyze the potentially confounding effect of class size on the associations between object-oriented（OO）metrics and maintainability.To draw as many general conclusions as possible,the confounding effect of class size is analyzed on 127 C＋＋ systems and 113 Java systems.For each OO metric,the indirect effect that represents the distortion of the association caused by class size and its variance for individual systems is first computed.Then,a statistical meta-analysis technique is used to compute the average indirect effect over all the systems and to determine if it is significantly different from zero.The experimental results show that the confounding effects of class size on the associations between OO metrics and maintainability generally exist,regardless of whatever size metric is used.Therefore,empirical studies validating OO metrics on maintainability should consider class size as a confounding variable.
基金Supported by the National Natural Science Foundation of China (Nos.U0933005,41076014,40906021,41176035)the National High Technology Research and Development Program of China (863 Program)(No.2007AA092001-02)
文摘A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorption coefficient. The modified model performed well using in-situ data from the northern South China Sea, and the results were reliable and accurate. The relative errors of the size-fractioned chlorophyll-a concentration for each size class were: micro-:21%, nano-:41%, pico-:26%, and nano+pico:23%. The model was then applied on ocean color remote sensing data to examine the distribution and variation of phytoplankton size classes in northern South China Sea on a large scale.
基金funded by the National Environmental Research Council, UK, through a PhD studentship at the Centre for observation of Air-Sea Interactions & fluXes (CASIX)the National Centre for Earth Observation and NERC Oceans 2025 programme (Themes 6 and 10)
文摘An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitoring the health of our oceans, and improving our understanding of the bio-geochemical interactions between phytoplankton and their environment. In this paper a new algorithm is developed for detecting three dominant phytoplankton size classes based on distinct differences in their optical signatures. The technique is validated against an independent coupled satellite reflectance and in situ pigment dataset and run on the 10-year NASA Sea viewing Wide Field of view Sensor （SeaWiFS） data series. Results indicate that on average 3.6% of the global oceanic surface layer is dominated by microplankton, 18.0% by nanoplankton and 78.4% by pieoplankton. Results, however, are seen to vary depending on season and ocean basin.
文摘Based on the theory of constructivism, this article analyzes the open problems colloquial English large size class teaching of higher vocational public English and points out the countermeasures of colloquial English large-scale class teaching of higher vocational public English.
基金Supported by the Youth Project of Hubei Provincial Department of Education (Q20112807)the Outstanding Young Team Project of Hubei Provincial Higher School (T201009)
文摘Let G be a finite group and π be a set of primes including at least two elements. We write cd（G） and cs（G） to denote the set of complex irreducible character degrees and conjugacy class sizes of G , respectively, and write π（m）to denote the set of all prime divisors of a positive integer m . For any 1≠m∈cd（G） and 1≠m∈cs（G）, in this note, we shall present the corresponding group structures of finite group G in the case π（m）=π , respectively, which generalizes the result of finite groups with character degrees of two distinct primes. Furthermore, we shall see that the influence of the two sets on the group structure is analogous.
基金supported by project funding from Chongqing Normal University (No. 12XLB009)Key Projects in the National Science & Technology Program (No. 2006BAD26B0302)
文摘Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were developed based on similar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSCStd and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and paleo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.
文摘Traditional classification algorithms perform not very well on imbalanced data sets and small sample size. To deal with the problem, a novel method is proposed to change the class distribution through adding virtual samples, which are generated by the windowed regression over-sampling (WRO) method. The proposed method WRO not only reflects the additive effects but also reflects the multiplicative effect between samples. A comparative study between the proposed method and other over-sampling methods such as synthetic minority over-sampling technique (SMOTE) and borderline over-sampling (BOS) on UCI datasets and Fourier transform infrared spectroscopy (FTIR) data set is provided. Experimental results show that the WRO method can achieve better performance than other methods.
文摘“Four classes of enterprises above designated size”(hereinafter called four-classes enterprises)refer to objects of statistical survey that have reached a certain scale in China’s current statistical method system,including four classes in national economy,namely,industrial enterprises above designated size,construction and real estate development and management enterprises above qualifications,wholesale and retail,catering and accommodation enterprises,and service enterprises above designated size,which are the primary part of national economic and social development activities.This paper is focused on analyzing the practice and difficulties in the current statistics work of four-classes enterprises,and then this paper proposes some recommendations.
文摘The characteristics of human figures, variable laws and the basic sizes of each part of human body have been found by means of anthropomctric measurements of the middle-arid old-aged men and data analyses. Also, proposals of how to classify dimensions and sizes of the medium human figures among the middle-and old-aged men have been put forward.
基金National Natural Science Foundation of China(52065036,52365018)Natural Science Foundation of Gansu(23JRRA760)+1 种基金Hongliu Outstanding Youth Foundation of Lanzhou University of TechnologyChina Postdoctoral Science Foundation(2023M733583)。
文摘The scratching mechanism of polycrystallineγ-TiAl alloy was investigated at the atomic scale using the molecular dynamics method,with a focus on the influence of different grain sizes.The analysis encompassed tribological characteristics,scratch morphology,subsurface defect distribution,temperature variations,and stress states during the scratching process.The findings indicate that the scratch force,number of recovered atoms,and pile-up height exhibit abrupt changes when the critical size is 9.41 nm due to the influence of the inverse Hall-Petch effect.Variations in the number of grain boundaries and randomness of grain orientation result in different accumulation patterns on the scratch surface.Notably,single crystal materials and those with 3.73 nm in grain size display more regular surface morphology.Furthermore,smaller grain size leads to an increase in average coefficient of friction,removed atoms number,and wear rate.While it also causes higher temperatures with a larger range of distributions.Due to the barrier effect of grain boundaries,smaller grains exhibit reduced microscopic defects.Additionally,average von Mises stress and hydrostatic compressive stress at the indenter tip decrease as grain size decreases owing to grain boundary obstruction.
基金supported by the National Key Research and Development Plan of the Ministry of Science and Technology,China(Grant No.:2022YFE0125300)the National Natural Science Foundation of China(Grant No:81690262)+2 种基金the National Science and Technology Major Project,China(Grant No.:2017ZX09201004-021)the Open Project of National facility for Translational Medicine(Shanghai),China(Grant No.:TMSK-2021-104)Shanghai Jiao Tong University STAR Grant,China(Grant Nos.:YG2022ZD024 and YG2022QN111).
文摘Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.
基金supported by the National Natural Science Foundation of China(Nos.52222110,52401354,and 52301353).
文摘As the main geomaterials for coral reefs oil or gas extraction and underground infrastructure construction,coral reef limestone demonstrates significantly distinct mechanical responses compared to terrigenous rocks.To investigate the mechanical behaviour of coral reef limestone under the coupling impact of size and strain rate,the uniaxial compression tests were conducted on reef limestone samples with length-to-diameter(L/D)ratio ranging from 0.5 to 2.0 at strain rate ranging from 10^(−5)·s^(−1)to 10^(−2)·s^(−1).It is revealed that the uniaxial compressive strength(UCS)and residual compressive strength(RCS)of coral reef limestone exhibits a decreasing trend with L/D ratio increasing.The dynamic increase factor(DIF)of UCS is linearly correlated with the logarithm of strain rate,while increasing the L/D ratio further enhances the DIF.The elastic modulus increases with strain rate or L/D ratio increasing,whereas the Poisson’s ratio approximates to a constant value of 0.24.The failure strain increases with strain rate increasing or L/D ratio decreasing,while the increase in L/D ratio will inhibit the enhancing effect of the strain rate.The high porosity and low mineral strength are the primary factors contributing to a high RCS of 16.7%–64.9%of UCS,a lower brittleness index and multiple irregular fracture planes.The failure pattern of coral reef limestone transits from the shear-dominated to the splitting-dominated failure with strain rate increasing or L/D ratio decreasing,which is mainly governed by the constrained zones induced by end friction and the strain rate-dependent crack propagation.Moreover,a predictive formula incorporating coupling effect of size and strain rate for the UCS of reef limestone was established and verified to effectively capture the trend of UCS.
文摘Understanding the fracture behavior of rocks subjected to temperature and accounting for the rock's texture is vital for safe and efficient design.Prior studies have often focused on isolated aspects of rock fracture behavior,neglecting the combined influence of grain size and temperature on fracture behavior.This study employs specimens based on the particle flow code-grain based model to scrutinize the influence of temperature and grain size discrepancies on the fracture characteristics of sandstone.In pursuit of this goal,we manufactured ninety-six semi-circular bend specimens with grain sizes spanning from 0.5 mm to 1.5 mm,predicated on the mineral composition of sandstone.Recognizing the significance of intra-granular and inter-granular fractures,the grains were considered deformable and susceptible to breakage.The numerical model was calibrated using the results of uniaxial compressive strength(UCS)and Brazilian tests.We implemented thermo-mechanical coupled analysis to simulate mode Ⅰ,mode Ⅱ,and mixed mode(Ⅰ-Ⅱ)fracture toughness tests and subsequently studied alterations in the fracture behavior of sandstone at temperatures from 25℃ to 700℃.Our findings revealed increased fracture toughness as the temperature escalated from 25℃ to 200℃.However,beyond the threshold of 200℃,we noted a decline in fracture toughness.More specifically,the drop in mode Ⅰ fracture toughness was more pronounced in specimens with finer grains than those with coarser grains.Contrarily,the trend was reversed for mode Ⅱ fracture toughness.In contrast,the reduction of mixed mode(Ⅰ-Ⅱ)fracture toughness seemed almost linear across all grain sizes.Furthermore,we identified a correlation between temperature and grain size and their collective impact on crack propagation patterns.Comparing our results with established theoretical benchmarks,we confirmed that both temperature and grain size variations influence the fracture envelopes of sandstone.
