Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.Ho...Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.展开更多
The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited stand...The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.展开更多
Objective:To labelavidin(Av)or streptavidin(SA)with 153 Sm by takingadvantageof thehighbindingaffin-ityof biotinto Av or SA.Methods:A biotinderivative(DTPA-biotin)wasradiolabelledwith 153 Sm andthenboundto Av or SA.Th...Objective:To labelavidin(Av)or streptavidin(SA)with 153 Sm by takingadvantageof thehighbindingaffin-ityof biotinto Av or SA.Methods:A biotinderivative(DTPA-biotin)wasradiolabelledwith 153 Sm andthenboundto Av or SA.Thein vivo kineticsandbiodistributionof 153 Sm-labeledAv,SA andDTPA-biotinwerestudiedinratsandmice.Results:153 Sm-Avwascharacterizedby rapidclearancefromthebloodwithhighliverandrenaluptake;153 Sm-SAwas clearedfromthebloodslowlywithhighretentionintheliver,spleenandkidney,whereas 153 Sm-DTPA-biotinmetabolismwas accelerated,anditsexcretionwasmainlythroughthekidney.Conclu sion:Thebiodistributiondifferenceof SAandAvmay providean experimentalbasisfor theselectionof differentcomponentsof avidin-biotinsystemin pretagetingradioim-munoimagingandradioimmunotherapy.展开更多
Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in emb...Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.展开更多
The health effects of ambient PM 2.5 and its potential mechanisms have generated considerable interest.In vitro cell studies and ex vivo animal experiments may not accurately determine the characteristics of PM 2.5 pa...The health effects of ambient PM 2.5 and its potential mechanisms have generated considerable interest.In vitro cell studies and ex vivo animal experiments may not accurately determine the characteristics of PM 2.5 particles.To better understand their detailed mechanisms,we performed an in vivo study using single photon emission tomography(SPECT)imaging.To mimic the PM 2.5 particles,SiO2 nanoparticles modified by ethylene carbonate or polyvinyl pyrrolidone were labeled with 131I.After administration via inhalation,in vivo SPECT imaging of the radiolabeled particles in sprague dawley rats was performed.It was found that radioactivity accumulated in the lungs and trachea 6 and 24 h after administration.In addition,significant radioactivity was observed in the abdomen,including the liver and kidneys.The results were also confirmed by ex vivo autoradiography.This study revealed that in vivo SPECT imaging could be an effective method for investigating the properties of PM 2.5 particles.展开更多
In this work,we propose a comprehensive theoretical framework for the multilevel NAND(NOT AND logic)flash memory,built upon the modified Student’s t distribution where the distortion of the threshold voltage caused b...In this work,we propose a comprehensive theoretical framework for the multilevel NAND(NOT AND logic)flash memory,built upon the modified Student’s t distribution where the distortion of the threshold voltage caused by the random telegraph noise,cell-to-cell interference and data retention noise are jointly considered.Based on the superposition modulation,we build a non-orthogonal multiuser communication model where a linear mapping is conducted between the verify voltages and binary antipodal symbols.Aimed at improving the storage efficiency,we propose an unequal amplitude mapping(UAM)solution by optimizing the weighting coefficients of verify voltages to intelligently adjust the width of each state.Moreover,the uniform storage efficiency region and sum storage efficiency of different labelings with various decoding schemes are discussed.Simulation results validate the effectiveness of our proposed UAM solution where an up to 20.9%storage efficiency gain can be achieved compared to the current used benchmark scheme.In addition,analytical and simulation results also demonstrate that the successive cancellation decoding outperforms other decoding schemes for all labelings.展开更多
The Nutri-Score is a 5-color front-of-pack nutrition label designed to provide consumers with an easily understandable guideline to the healthiness of food products.The impact that the Nutri-Score may have on consumer...The Nutri-Score is a 5-color front-of-pack nutrition label designed to provide consumers with an easily understandable guideline to the healthiness of food products.The impact that the Nutri-Score may have on consumers'choices is unclear since different experimental paradigms have found vastly different effect sizes.In the present study,we have investigated how student participants change a hypothetical personal 1-daydietary plan after a learning phase during which they learn about the Nutri-Scores of the available food items.Participants were instructed to compose a healthy diet plan in order that the question of whether the NutriScore would improve their ability to compose a healthy dietary plan could be investigated,independent of the question of whether they would apply this knowledge in their ordinary lives.We found a substantial(Cohen's d=0.86)positive impact on nutritional quality(as measured by the Nutrient Profiling System score of the Food Standards Agency)and a medium-sized(Cohen's d=0.43)reduction of energy content.Energy content reduction was larger for participants who had initially composed plans with higher energy content.The results suggest that the Nutri-Score has the potential to guide consumers to healthier food choices.It remains unclear,however,whether this potential will be reflected in real-life dietary choices.展开更多
Understanding and monitoring the cross-contamination of food allergens is crucial for safeguarding public health and ensuring food safety.Food allergen risk assessment,derived from classical toxicological principles,c...Understanding and monitoring the cross-contamination of food allergens is crucial for safeguarding public health and ensuring food safety.Food allergen risk assessment,derived from classical toxicological principles,can identify and quantify the risk of allergies.This study aimed to investigate the risk of wheat allergic reactions to prepackaged foods from China through the utilization of food allergen risk assessment.A total of 575 products have been surveyed,wheat/gluten,milk and egg were major allergens labelled on products.According to voluntary incidental trace allergen labelling 3.0(VITAL®3.0)program,the number of products belonged to Action Level 2 were 303.Integration of precautionary allergen labeling(PAL)analysis indicated that 9.57%products would pose a potential risk to wheat allergic individuals.The probabilistic risk assessment results suggest that 7984 allergic reactions may arise among wheat-allergic consumers during 10000 eating occasions due to the consumption of pre-packaged food products with incorrect wheat-related allergen labelling.This study demonstrated that a risk assessment-based approach can support the guidance of allergen labelling and management of food allergen for pre-packaged food products,providing protection for allergic individuals in food consumption and for food manufacturers in food production and trade.展开更多
With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recogni...With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.展开更多
Achieving increasingly finely targeted drug delivery to organs,tissues,cells,and even to intracellular biomacromolecules is one of the core goals of nanomedicines.As the delivery destination is refined to cellular and...Achieving increasingly finely targeted drug delivery to organs,tissues,cells,and even to intracellular biomacromolecules is one of the core goals of nanomedicines.As the delivery destination is refined to cellular and subcellular targets,it is essential to explore the delivery of nanomedicines at the molecular level.However,due to the lack of technical methods,the molecular mechanism of the intracellular delivery of nanomedicines remains unclear to date.Here,we develop an enzyme-induced proximity labeling technology in nanoparticles(nano-EPL)for the real-time monitoring of proteins that interact with intracellular nanomedicines.Poly(lactic-co-glycolic acid)nanoparticles coupled with horseradish peroxidase(HRP)were fabricated as a model(HRP(+)-PNPs)to evaluate the molecular mechanism of nano delivery in macrophages.By adding the labeling probe biotin-phenol and the catalytic substrate H_(2)O_(2)at different time points in cellular delivery,nano-EPL technology was validated for the real-time in situ labeling of proteins interacting with nanoparticles.Nano-EPL achieves the dynamic molecular profiling of 740 proteins to map the intracellular delivery of HRP(+)-PNPs in macrophages over time.Based on dynamic clustering analysis of these proteins,we further discovered that different organelles,including endosomes,lysosomes,the endoplasmic reticulum,and the Golgi apparatus,are involved in delivery with distinct participation timelines.More importantly,the engagement of these organelles differentially affects the drug delivery efficiency,reflecting the spatial–temporal heterogeneity of nano delivery in cells.In summary,these findings highlight a significant methodological advance toward understanding the molecular mechanisms involved in the intracellular delivery of nanomedicines.展开更多
Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on t...Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.展开更多
A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused ...A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused by inaccurate labeling.To mitigate these issues,an improved training framework has been proposed.In this approach,soft labels from previous training serve as teachers to supervise the further learning process;this has lead to a significant improvement in predictive model performance.Notably,this enhancement is primarily attributed to the coupling effect of the soft labels and correction mechanism.This improved training framework introduces an instance-specific label smoothing method,which reflects a more nuanced model assessment on the likelihood of a disruption.It presents a possible solution to effectively address the challenges associated with accurate labeling across different machines.展开更多
Fe-N-doped carbon materials(Fe-N-C)are promising candidates for oxygen reduction reaction(ORR)relative to Pt-based catalysts in proton exchange membrane fuel cells(PEMFCs).However,the intrinsic contributions of Fe-N_(...Fe-N-doped carbon materials(Fe-N-C)are promising candidates for oxygen reduction reaction(ORR)relative to Pt-based catalysts in proton exchange membrane fuel cells(PEMFCs).However,the intrinsic contributions of Fe-N_(4)moiety with different chemical/spin states(e.g.D1,D2,D3)to ORR are unclear since various states coexist inevitably.In the present work,Fe-N-C core-shell nanocatalyst with single lowspin Fe(Ⅱ)-N_(4)species(D1)is synthesized and identified with ex-situ ultralow temperature Mossbauer spectroscopy(T=1.6 K)that could essentially differentiate various Fe-N_(4)states and invisible Fe-O species.By quantifying with CO-pulse chemisorption,site density and turnover frequency of Fe-N-C catalysts reach 2.4×10^(-9)site g^(-1)and 23 e site~(-1)s^(-1)during the ORR,respectively.Half-wave potential(0.915V_(RHE))of the Fe-N-C catalyst is more positive(approximately 54 mV)than that of Pt/C.Moreover,we observe that the performance of PEMFCs on Fe-N-C almost achieves the 2025 target of the US Department of Energy by demonstrating a current density of 1.037 A cm^(-2)combined with the peak power density of 0,685 W cm^(-2),suggesting the critical role of Fe(Ⅱ)-N_(4)site(D1).After 500 h of running,PEMFCs still deliver a power density of 1.26 W cm^(-2)at 1.0 bar H_(2)-O_(2),An unexpected rate-determining step is figured out by isotopic labelling experiment and theoretical calculation.This work not only offers valuable insights regarding the intrinsic contribution of Fe-N_(4)with a single spin state to alkaline/acidic ORR,but also provides great opportunities for developing high-performance stable PEMFCs.展开更多
Understanding the physiological adaptations of non-treeline trees to environmental stress is important to understand future shifts in species composition and distribution of current treeline ecotone.The aim of the pre...Understanding the physiological adaptations of non-treeline trees to environmental stress is important to understand future shifts in species composition and distribution of current treeline ecotone.The aim of the present study was to elucidate the mechanisms of the formation of the upper elevation limit of non-treeline tree species,Picea jezoensis,and the carbon allocation strategies of the species on Changbai Mountain.We employed the^(13)C in situ pulse labeling technique to trace the distribution of photosynthetically assimilated carbon in Picea jezoensis at different elevational positions(tree species at its upper elevation limit(TSAUE,1,700 m a.s.l.)under treeline ecotone;tree species at a lower elevation position(TSALE,1,400 m a.s.l.).We analyzed^(13)C and the non-structural carbohydrate(NSC)concentrations in various tissues following labeling.Our findings revealed a significant shift in carbon allocation in TSAUE compared to TSALE.There was a pronounced increase inδ^(13)C allocation to belowground components(roots,soil,soil respiration)in TSAUE compared to TSALE.Furthermore,the C flow rate within the plant-soil-atmosphere system was faster,and the C residence time in the plant was shorter in TSAUE.The trends indicate enhanced C sink activity in belowground tissues in TSAUE,with newly assimilated C being preferentially directed there,suggesting a more conservative C allocation strategy by P.jezoensis at higher elevations under harsher environments.Such a strategy,prioritizing C storage in roots,likely aids in withstanding winter cold stress at the expense of aboveground growth during the growing season,leading to reduced growth of TSAUE compared to TSALE.The results of the present study shed light on the adaptive mechanisms governing the upper elevation limits of non-treeline trees,and enhances our understanding of how non-treeline species might respond to ongoing climate change.展开更多
Recent advances in utilizing ^(17)O isotopic labeling methods for solid-state nuclear magnetic resonance(NMR)investigations of metal oxides for lithium-ion batteries have yielded extensive insights into their structur...Recent advances in utilizing ^(17)O isotopic labeling methods for solid-state nuclear magnetic resonance(NMR)investigations of metal oxides for lithium-ion batteries have yielded extensive insights into their structural and dynamic details.Herein,we commence with a brief introduction to recent research on lithium-ion battery oxide materials studied using ^(17)O solid-state NMR spectroscopy.Then we delve into a review of ^(17)O isotopic labeling methods for tagging oxygen sites in both the bulk and surfaces of metal oxides.At last,the unresolved problems and the future research directions for advancing the ^(17)O labeling technique are discussed.展开更多
Aim To separate texts from graphics on mechanical engineering drawings. Methods By improved labeling run, the size of circumscribed rectangle and pixel density about the connected graphs on a drawing were gained,...Aim To separate texts from graphics on mechanical engineering drawings. Methods By improved labeling run, the size of circumscribed rectangle and pixel density about the connected graphs on a drawing were gained, according to these value, texts not sticking to graphics could be separated from a drawing. The concept of appendant block was introduced, using the appendant block, texts sticking to graphics could be determined and cut. Results Texts and symbols on mechanical engineering drawings can be separated well. Conclusion This method is simple and fast, it does not destroy the shape of texts and symbols, it can be used for separating texts and symbols from all linear graphics.展开更多
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[Grant No.52222708]the Natural Science Foundation of Beijing Municipality[Grant No.3212033]。
文摘Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.
基金supported by National Natural Science Foundation of China (No. 51674032)
文摘The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.
文摘Objective:To labelavidin(Av)or streptavidin(SA)with 153 Sm by takingadvantageof thehighbindingaffin-ityof biotinto Av or SA.Methods:A biotinderivative(DTPA-biotin)wasradiolabelledwith 153 Sm andthenboundto Av or SA.Thein vivo kineticsandbiodistributionof 153 Sm-labeledAv,SA andDTPA-biotinwerestudiedinratsandmice.Results:153 Sm-Avwascharacterizedby rapidclearancefromthebloodwithhighliverandrenaluptake;153 Sm-SAwas clearedfromthebloodslowlywithhighretentionintheliver,spleenandkidney,whereas 153 Sm-DTPA-biotinmetabolismwas accelerated,anditsexcretionwasmainlythroughthekidney.Conclu sion:Thebiodistributiondifferenceof SAandAvmay providean experimentalbasisfor theselectionof differentcomponentsof avidin-biotinsystemin pretagetingradioim-munoimagingandradioimmunotherapy.
基金supported by the National Natural Science Foundation of China under Grants No.61534002,No.61761136015,No.61701095.
文摘Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.
基金supported by the National Natural Science Foundation of China(Nos.31671035,51803082)National Significant New Drugs Creation Program(No.2017ZX09304021)+1 种基金Jiangsu Province Foundation(Nos.BK20170204,BK20161137)Jiangsu Provincial Medical Innovation Team(Nos.CXTDA2017024,LGY2017088,QNRC2016628)。
文摘The health effects of ambient PM 2.5 and its potential mechanisms have generated considerable interest.In vitro cell studies and ex vivo animal experiments may not accurately determine the characteristics of PM 2.5 particles.To better understand their detailed mechanisms,we performed an in vivo study using single photon emission tomography(SPECT)imaging.To mimic the PM 2.5 particles,SiO2 nanoparticles modified by ethylene carbonate or polyvinyl pyrrolidone were labeled with 131I.After administration via inhalation,in vivo SPECT imaging of the radiolabeled particles in sprague dawley rats was performed.It was found that radioactivity accumulated in the lungs and trachea 6 and 24 h after administration.In addition,significant radioactivity was observed in the abdomen,including the liver and kidneys.The results were also confirmed by ex vivo autoradiography.This study revealed that in vivo SPECT imaging could be an effective method for investigating the properties of PM 2.5 particles.
基金supported by Key Project of Sichuan Provincial Natural Science Foundation(No.2022NSFSC0043).
文摘In this work,we propose a comprehensive theoretical framework for the multilevel NAND(NOT AND logic)flash memory,built upon the modified Student’s t distribution where the distortion of the threshold voltage caused by the random telegraph noise,cell-to-cell interference and data retention noise are jointly considered.Based on the superposition modulation,we build a non-orthogonal multiuser communication model where a linear mapping is conducted between the verify voltages and binary antipodal symbols.Aimed at improving the storage efficiency,we propose an unequal amplitude mapping(UAM)solution by optimizing the weighting coefficients of verify voltages to intelligently adjust the width of each state.Moreover,the uniform storage efficiency region and sum storage efficiency of different labelings with various decoding schemes are discussed.Simulation results validate the effectiveness of our proposed UAM solution where an up to 20.9%storage efficiency gain can be achieved compared to the current used benchmark scheme.In addition,analytical and simulation results also demonstrate that the successive cancellation decoding outperforms other decoding schemes for all labelings.
文摘The Nutri-Score is a 5-color front-of-pack nutrition label designed to provide consumers with an easily understandable guideline to the healthiness of food products.The impact that the Nutri-Score may have on consumers'choices is unclear since different experimental paradigms have found vastly different effect sizes.In the present study,we have investigated how student participants change a hypothetical personal 1-daydietary plan after a learning phase during which they learn about the Nutri-Scores of the available food items.Participants were instructed to compose a healthy diet plan in order that the question of whether the NutriScore would improve their ability to compose a healthy dietary plan could be investigated,independent of the question of whether they would apply this knowledge in their ordinary lives.We found a substantial(Cohen's d=0.86)positive impact on nutritional quality(as measured by the Nutrient Profiling System score of the Food Standards Agency)and a medium-sized(Cohen's d=0.43)reduction of energy content.Energy content reduction was larger for participants who had initially composed plans with higher energy content.The results suggest that the Nutri-Score has the potential to guide consumers to healthier food choices.It remains unclear,however,whether this potential will be reflected in real-life dietary choices.
基金supported by the Central Government Guide Local Special Fund Project for Scientific and Technological Development of Jiangxi Province(20221ZDD02001).
文摘Understanding and monitoring the cross-contamination of food allergens is crucial for safeguarding public health and ensuring food safety.Food allergen risk assessment,derived from classical toxicological principles,can identify and quantify the risk of allergies.This study aimed to investigate the risk of wheat allergic reactions to prepackaged foods from China through the utilization of food allergen risk assessment.A total of 575 products have been surveyed,wheat/gluten,milk and egg were major allergens labelled on products.According to voluntary incidental trace allergen labelling 3.0(VITAL®3.0)program,the number of products belonged to Action Level 2 were 303.Integration of precautionary allergen labeling(PAL)analysis indicated that 9.57%products would pose a potential risk to wheat allergic individuals.The probabilistic risk assessment results suggest that 7984 allergic reactions may arise among wheat-allergic consumers during 10000 eating occasions due to the consumption of pre-packaged food products with incorrect wheat-related allergen labelling.This study demonstrated that a risk assessment-based approach can support the guidance of allergen labelling and management of food allergen for pre-packaged food products,providing protection for allergic individuals in food consumption and for food manufacturers in food production and trade.
基金supported in part by the National Natural Science Foundation of China under Grant No.62171334,No.11973077 and No.12003061。
文摘With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.
基金supported by Natural Science Foundation of Beijing Municipality(L212013)National Key Research and Development Program of China(No.2022YFA1206104)+2 种基金AI+Health Collaborative Innovation Cultivation Project(Z211100003521002)National Natural Science Foundation of China(81971718,82073786,81872809,U20A20412,81821004)Beijing Natural Science Foundation(7222020).
文摘Achieving increasingly finely targeted drug delivery to organs,tissues,cells,and even to intracellular biomacromolecules is one of the core goals of nanomedicines.As the delivery destination is refined to cellular and subcellular targets,it is essential to explore the delivery of nanomedicines at the molecular level.However,due to the lack of technical methods,the molecular mechanism of the intracellular delivery of nanomedicines remains unclear to date.Here,we develop an enzyme-induced proximity labeling technology in nanoparticles(nano-EPL)for the real-time monitoring of proteins that interact with intracellular nanomedicines.Poly(lactic-co-glycolic acid)nanoparticles coupled with horseradish peroxidase(HRP)were fabricated as a model(HRP(+)-PNPs)to evaluate the molecular mechanism of nano delivery in macrophages.By adding the labeling probe biotin-phenol and the catalytic substrate H_(2)O_(2)at different time points in cellular delivery,nano-EPL technology was validated for the real-time in situ labeling of proteins interacting with nanoparticles.Nano-EPL achieves the dynamic molecular profiling of 740 proteins to map the intracellular delivery of HRP(+)-PNPs in macrophages over time.Based on dynamic clustering analysis of these proteins,we further discovered that different organelles,including endosomes,lysosomes,the endoplasmic reticulum,and the Golgi apparatus,are involved in delivery with distinct participation timelines.More importantly,the engagement of these organelles differentially affects the drug delivery efficiency,reflecting the spatial–temporal heterogeneity of nano delivery in cells.In summary,these findings highlight a significant methodological advance toward understanding the molecular mechanisms involved in the intracellular delivery of nanomedicines.
基金the Natural Science Foundation of China(Grant Numbers 72074014 and 72004012).
文摘Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.
基金supported by National Natural Science Foundation of China(Nos.12175277 and 11975271)the National Key R&D Program of China(No.2022YFE 03050003)。
文摘A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused by inaccurate labeling.To mitigate these issues,an improved training framework has been proposed.In this approach,soft labels from previous training serve as teachers to supervise the further learning process;this has lead to a significant improvement in predictive model performance.Notably,this enhancement is primarily attributed to the coupling effect of the soft labels and correction mechanism.This improved training framework introduces an instance-specific label smoothing method,which reflects a more nuanced model assessment on the likelihood of a disruption.It presents a possible solution to effectively address the challenges associated with accurate labeling across different machines.
基金financial support from the“Hundred Talents Program”of the Chinese Academy of Sciencesthe“Young Talents Training Program”of the Shanghai Branch of the Chinese Academy of Sciences+3 种基金the financial support from the Xiamen City Natural Science Foundation of China(3502Z20227085,3502Z20227256)the National Science Youth Foundation of China(22202205)the Fujian Provincial Natural Science Foundation of China(2022J01502)Open Source Foundation of State Key Laboratory of Structural Chemistry。
文摘Fe-N-doped carbon materials(Fe-N-C)are promising candidates for oxygen reduction reaction(ORR)relative to Pt-based catalysts in proton exchange membrane fuel cells(PEMFCs).However,the intrinsic contributions of Fe-N_(4)moiety with different chemical/spin states(e.g.D1,D2,D3)to ORR are unclear since various states coexist inevitably.In the present work,Fe-N-C core-shell nanocatalyst with single lowspin Fe(Ⅱ)-N_(4)species(D1)is synthesized and identified with ex-situ ultralow temperature Mossbauer spectroscopy(T=1.6 K)that could essentially differentiate various Fe-N_(4)states and invisible Fe-O species.By quantifying with CO-pulse chemisorption,site density and turnover frequency of Fe-N-C catalysts reach 2.4×10^(-9)site g^(-1)and 23 e site~(-1)s^(-1)during the ORR,respectively.Half-wave potential(0.915V_(RHE))of the Fe-N-C catalyst is more positive(approximately 54 mV)than that of Pt/C.Moreover,we observe that the performance of PEMFCs on Fe-N-C almost achieves the 2025 target of the US Department of Energy by demonstrating a current density of 1.037 A cm^(-2)combined with the peak power density of 0,685 W cm^(-2),suggesting the critical role of Fe(Ⅱ)-N_(4)site(D1).After 500 h of running,PEMFCs still deliver a power density of 1.26 W cm^(-2)at 1.0 bar H_(2)-O_(2),An unexpected rate-determining step is figured out by isotopic labelling experiment and theoretical calculation.This work not only offers valuable insights regarding the intrinsic contribution of Fe-N_(4)with a single spin state to alkaline/acidic ORR,but also provides great opportunities for developing high-performance stable PEMFCs.
基金supported by the National Natural Science Foundation of China(Grant numbers 4237105242271100+3 种基金4197112442371095)the Natural Science Foundation of Jilin Province,China(Nos.YDZJ202201ZYTS483YDZJ202201ZYTS470)。
文摘Understanding the physiological adaptations of non-treeline trees to environmental stress is important to understand future shifts in species composition and distribution of current treeline ecotone.The aim of the present study was to elucidate the mechanisms of the formation of the upper elevation limit of non-treeline tree species,Picea jezoensis,and the carbon allocation strategies of the species on Changbai Mountain.We employed the^(13)C in situ pulse labeling technique to trace the distribution of photosynthetically assimilated carbon in Picea jezoensis at different elevational positions(tree species at its upper elevation limit(TSAUE,1,700 m a.s.l.)under treeline ecotone;tree species at a lower elevation position(TSALE,1,400 m a.s.l.).We analyzed^(13)C and the non-structural carbohydrate(NSC)concentrations in various tissues following labeling.Our findings revealed a significant shift in carbon allocation in TSAUE compared to TSALE.There was a pronounced increase inδ^(13)C allocation to belowground components(roots,soil,soil respiration)in TSAUE compared to TSALE.Furthermore,the C flow rate within the plant-soil-atmosphere system was faster,and the C residence time in the plant was shorter in TSAUE.The trends indicate enhanced C sink activity in belowground tissues in TSAUE,with newly assimilated C being preferentially directed there,suggesting a more conservative C allocation strategy by P.jezoensis at higher elevations under harsher environments.Such a strategy,prioritizing C storage in roots,likely aids in withstanding winter cold stress at the expense of aboveground growth during the growing season,leading to reduced growth of TSAUE compared to TSALE.The results of the present study shed light on the adaptive mechanisms governing the upper elevation limits of non-treeline trees,and enhances our understanding of how non-treeline species might respond to ongoing climate change.
基金supported by National Key R&D Program of China(2021YFA1502803)the National Natural Science Foundation of China(NSFC)(21972066,91745202)+3 种基金NSFC-Royal Society Joint Program(21661130149)L.P.thanks the Royal Society and Newton Fund for a Royal Society-Newton Advanced Fellowshipsupported by the Research Funds for the Frontiers Science Centre for Critical Earth Material Cycling,Nanjing Universitya Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Recent advances in utilizing ^(17)O isotopic labeling methods for solid-state nuclear magnetic resonance(NMR)investigations of metal oxides for lithium-ion batteries have yielded extensive insights into their structural and dynamic details.Herein,we commence with a brief introduction to recent research on lithium-ion battery oxide materials studied using ^(17)O solid-state NMR spectroscopy.Then we delve into a review of ^(17)O isotopic labeling methods for tagging oxygen sites in both the bulk and surfaces of metal oxides.At last,the unresolved problems and the future research directions for advancing the ^(17)O labeling technique are discussed.
文摘Aim To separate texts from graphics on mechanical engineering drawings. Methods By improved labeling run, the size of circumscribed rectangle and pixel density about the connected graphs on a drawing were gained, according to these value, texts not sticking to graphics could be separated from a drawing. The concept of appendant block was introduced, using the appendant block, texts sticking to graphics could be determined and cut. Results Texts and symbols on mechanical engineering drawings can be separated well. Conclusion This method is simple and fast, it does not destroy the shape of texts and symbols, it can be used for separating texts and symbols from all linear graphics.
