In this letter, the self-assembling process of thioglycollic acid on gold in aqueous solutions was studied by QCM technique. Frequency information suggests bilayer or multilayer adsorbate formation in 0.5 mol/L HCl so...In this letter, the self-assembling process of thioglycollic acid on gold in aqueous solutions was studied by QCM technique. Frequency information suggests bilayer or multilayer adsorbate formation in 0.5 mol/L HCl solution but only monolayer one in 0.5 mol/L KOH solution. This phenomenon is explained by hydrogen bond effect.展开更多
Self-assembly of metal halide perovskite nanocrystals(NCs)into superlattices can exhibit unique collective properties,which have significant application values in the display,detector,and solar cell field.This review ...Self-assembly of metal halide perovskite nanocrystals(NCs)into superlattices can exhibit unique collective properties,which have significant application values in the display,detector,and solar cell field.This review discusses the driving forces behind the self-assembly process of perovskite NCs,and the commonly used self-assembly methods and different self-assembly structures are detailed.Subsequently,we summarize the collective optoelectronic properties and application areas of perovskite superlattice structures.Finally,we conclude with an outlook on the potential issues and future challenges in developing perovskite NCs.展开更多
The interactions between lignin oligomers and solvents determine the behaviors of lignin oligomers self-assembling into uniform lignin nanoparticles(LNPs).Herein,several alcohol solvents,which readily interact with th...The interactions between lignin oligomers and solvents determine the behaviors of lignin oligomers self-assembling into uniform lignin nanoparticles(LNPs).Herein,several alcohol solvents,which readily interact with the lignin oligomers,were adopted to study their effects during solvent shifting process for LNPs’production.The lignin oligomers with widely distributed molecular weight and abundant guaiacyl units were extracted from wood waste(mainly consists of pine wood),exerting outstanding self-assembly capability.Uniform and spherical LNPs were generated in H_(2)O-n-propanol cosolvent,whereas irregular LNPs were obtained in H_(2)O-methanol cosolvent.The unsatisfactory self-assembly performance of the lignin oligomers in H_(2)O-methanol cosolvent could be attributed to two aspects.On one hand,for the initial dissolution state,the distinguishing Hansen solubility parameter and polarity between methanol solvent and lignin oligomers resulted in the poor dispersion of the lignin oligomers.On the other hand,strong hydrogen bonds between methanol solvent and lignin oligomers during solvent shifting process,hindered the interactions among the lignin oligomers for self-assembly.展开更多
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control ...This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.展开更多
The abuse of plastic food packaging has brought about severe white pollution issues around the world.Developing green and sustainable biomass packaging is an effective way to solve this problem.Hence,a chitosan/sodium...The abuse of plastic food packaging has brought about severe white pollution issues around the world.Developing green and sustainable biomass packaging is an effective way to solve this problem.Hence,a chitosan/sodium alginate-based multilayer film is fabricated via a layer-by-layer(LBL)self-assembly method.With the help of superior interaction between the layers,the multilayer film possesses excellent mechanical properties(with a tensile strength of 50 MPa).Besides,the film displays outstanding water retention property(blocking moisture of 97.56%)and ultraviolet blocking property.Anthocyanin is introduced into the film to detect the food quality since it is one natural plant polyphenol that is sensitive to the pH changes ranging from 1 to 13 in food when spoilage occurs.It is noted that the film is also bacteriostatic which is desired for food packaging.This study describes a simple technique for the development of advanced multifunctional and fully biodegradable food packaging film and it is a sustainable alternative to plastic packaging.展开更多
We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed proces...We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.展开更多
Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation indust...Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.展开更多
Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a w...Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).展开更多
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b...Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.展开更多
Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and oper...Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.展开更多
The hydroxyl-terminated self-assembled monolayer(OH-SAM),as a surface resistant to protein adsorption,exhibits substantial potential in applications such as ship navigation and medical implants,and the appropriate str...The hydroxyl-terminated self-assembled monolayer(OH-SAM),as a surface resistant to protein adsorption,exhibits substantial potential in applications such as ship navigation and medical implants,and the appropriate strategies for designing anti-fouling surfaces are crucial.Here,we employ molecular dynamics simulations and alchemical free energy calculations to systematically analyze the factors influencing resistance to protein adsorption on the SAMs terminated with single or double OH groups at three packing densities(∑=2.0 nm^(-2),4.5 nm^(-2),and 6.5 nm^(-2)),respectively.For the first time,we observed that the compactness and order of interfacial water enhance its physical barrier effect,subsequently enhancing the resistance of SAM to protein adsorption.Notably,the spatial hindrance effect of SAM leads to the embedding of protein into SAM,resulting in a lack of resistance of SAM towards protein.Furthermore,the number of hydroxyl groups per unit area of double OH-terminated SAM at ∑=6.5 nm^(-2) is approximately 2 to 3 times that of single OH-terminated SAM at ∑=6.5 nm^(-2) and 4.5 nm^(-2),consequently yielding a weaker resistance of double OH-terminated SAM towards protein.Meanwhile,due to the structure of SAM itself,i.e.,the formation of a nearly perfect ice-like hydrogen bond structure,the SAM exhibits the weakest resistance towards protein.This study will complement and improve the mechanism of OH-SAM resistance to protein adsorption,especially the traditional barrier effect of interfacial water.展开更多
Bio-based cyclodextrins(CDs)are a common research object in supramolecular chemistry.The special cavity structure of CDs can form supramolecular self-assemblies such as vesicles and microcrystals through weak interact...Bio-based cyclodextrins(CDs)are a common research object in supramolecular chemistry.The special cavity structure of CDs can form supramolecular self-assemblies such as vesicles and microcrystals through weak interaction with guest molecules.The different forms of supramolecular self-assemblies can be transformed into each other under certain conditions.The regulation of supramolecular self-assembly is not only helpful to understand the self-assembly principle,but also beneficial to its application.In the present study,the self-assembly behavior of epoxy-β-cyclodextrin(EP-β-CD)and mixed anionic and cationic surfactant system(sodium dodecyl sulfate/dodecyltrimethylammonium bromide,SDS/DTAB)in aqueous solution was studied.Morphological and particle size characterization found that the SDS/DTAB@EP-β-CD complex,as the basic building unit,self-assembled into worm-like micelles at lower temperatures and vesicles at higher temperatures.Nuclear magnetic resonance(NMR)and Fourier transform infrared spectroscopy(FT-IR)analysis revealed that the driving force for the formation of vesicles and worm-like micelles was the hydrogen bonds between EP-β-CD molecules,while water molecules played an important role in promoting vesicle formation between SDS/DTAB@EP-β-CD units.Herein,the mechanism of the morphologic transformation of SDS/DTAB@EP-β-CD supramolecular aggregates induced by temperature was elucidated by exploring the self-assembly process,which may provide an excellent basis for the development of delivery carriers.展开更多
In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technologica...In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.展开更多
Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface mo...Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface morphology and electric property was investigated.The results confirmed the dense membrane of PEDOT:PSS and the lamellar structure of PDDA-rGO on the fibers.It has excellent electrical conductivity and mechanical properties.The fiber based electrochemical transistor(FECTs)prepared by the composite conductive fiber has a maximum output current of 8.7 mA,a transconductance peak of 10 mS,an on time of 1.37 s,an off time of 1.6 s and excellent switching stability.Most importantly,the devices by layer by layer self-assembly technology opens a path for the true integration of organic electronics with traditional textile technologies and materials,laying the foundation for their later widespread application.展开更多
In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the in...In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.展开更多
Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics.Machine learning provides an innovative approach to exploring complex physical problems.In this study,the nuc...Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics.Machine learning provides an innovative approach to exploring complex physical problems.In this study,the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process.First,the binding energies for 2238 nuclei with Z>20 and N>20 are calculated using the Gaussian process in a physically motivated feature space,yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV.The results show the good learning ability of the Gaussian process in the studies of binding energies.Then,the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020.The theoretical results are in good agreement with the experimental data,reflecting the good predictive power of the Gaussian process.Moreover,the-decay energies for 1169 nuclei with 50≤Z≤110 are derived from the theoretical binding energies calculated using the Gaussian process.The average deviation and the standard deviation are,respectively,0.047 MeV and 0.070 MeV.Noticeably,the calculated-decay energies for the two new isotopes 204 Ac(Huang et al.Phys Lett B 834,137484(2022))and 207 Th(Yang et al.Phys Rev C 105,L051302(2022))agree well with the latest experimental data.These results demonstrate that the Gaussian pro-cess is reliable for the calculations of nuclear binding energies.Finally,the-decay properties of some unknown actinide nuclei are predicted using the Gaussian process.The predicted results can be useful guides for future research on binding energies and-decay properties.展开更多
Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches...Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized poi...Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.展开更多
文摘In this letter, the self-assembling process of thioglycollic acid on gold in aqueous solutions was studied by QCM technique. Frequency information suggests bilayer or multilayer adsorbate formation in 0.5 mol/L HCl solution but only monolayer one in 0.5 mol/L KOH solution. This phenomenon is explained by hydrogen bond effect.
基金financially supported by the National Key Research and Development Program of China (2021YFB3600403)the Fundamental Research Funds for the Central Universities (000-0903069032)。
文摘Self-assembly of metal halide perovskite nanocrystals(NCs)into superlattices can exhibit unique collective properties,which have significant application values in the display,detector,and solar cell field.This review discusses the driving forces behind the self-assembly process of perovskite NCs,and the commonly used self-assembly methods and different self-assembly structures are detailed.Subsequently,we summarize the collective optoelectronic properties and application areas of perovskite superlattice structures.Finally,we conclude with an outlook on the potential issues and future challenges in developing perovskite NCs.
基金supported by the National Natural Science Foundation of China(22078211)the China Postdoctoral Science Foundation(2022M721115).
文摘The interactions between lignin oligomers and solvents determine the behaviors of lignin oligomers self-assembling into uniform lignin nanoparticles(LNPs).Herein,several alcohol solvents,which readily interact with the lignin oligomers,were adopted to study their effects during solvent shifting process for LNPs’production.The lignin oligomers with widely distributed molecular weight and abundant guaiacyl units were extracted from wood waste(mainly consists of pine wood),exerting outstanding self-assembly capability.Uniform and spherical LNPs were generated in H_(2)O-n-propanol cosolvent,whereas irregular LNPs were obtained in H_(2)O-methanol cosolvent.The unsatisfactory self-assembly performance of the lignin oligomers in H_(2)O-methanol cosolvent could be attributed to two aspects.On one hand,for the initial dissolution state,the distinguishing Hansen solubility parameter and polarity between methanol solvent and lignin oligomers resulted in the poor dispersion of the lignin oligomers.On the other hand,strong hydrogen bonds between methanol solvent and lignin oligomers during solvent shifting process,hindered the interactions among the lignin oligomers for self-assembly.
基金supported in part by the Natural Sciences Engineering Research Council of Canada (NSERC)。
文摘This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.
基金National Undergraduate Training Program for Innovation and Entrepreneurship of China (Grant No.202210288027).
文摘The abuse of plastic food packaging has brought about severe white pollution issues around the world.Developing green and sustainable biomass packaging is an effective way to solve this problem.Hence,a chitosan/sodium alginate-based multilayer film is fabricated via a layer-by-layer(LBL)self-assembly method.With the help of superior interaction between the layers,the multilayer film possesses excellent mechanical properties(with a tensile strength of 50 MPa).Besides,the film displays outstanding water retention property(blocking moisture of 97.56%)and ultraviolet blocking property.Anthocyanin is introduced into the film to detect the food quality since it is one natural plant polyphenol that is sensitive to the pH changes ranging from 1 to 13 in food when spoilage occurs.It is noted that the film is also bacteriostatic which is desired for food packaging.This study describes a simple technique for the development of advanced multifunctional and fully biodegradable food packaging film and it is a sustainable alternative to plastic packaging.
基金partially supported by the National Natural Science Foundation of China(11871244)the Fundamental Research Funds for the Central Universities,JLU。
文摘We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.
基金supported in part by the National Key Research and Development Program of China(2021YFC2902703)the National Natural Science Foundation of China(62173078,61773105,61533007,61873049,61873053,61703085,61374147)。
文摘Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.
基金funded by CONAHCYT grant(252808)to GFCONAHCYT’s“Estancias Posdoctorales por México”program(662350)to HTB。
文摘Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).
基金the National Natural Science Foundation of China(62003298,62163036)the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009)the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。
文摘Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
基金supported by the National Key Research and Development Program of China (2020YFB1713800)the National Natural Science Foundation of China (92267205)+1 种基金the Hunan Provincial Innovation Foundation for Postgraduate (CX2022 0267)the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0181)。
文摘Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.
基金Project supported by the National Natural Science Foundation of China (Grants No. 12075201)the Science and Technology Planning Project of Jiangsu Province, China (Grant No. BK20201428)+1 种基金the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX21 3193)the Special Program for Applied Research on Supercomputation of the NSFC–Guangdong Joint Fund (the second phase)。
文摘The hydroxyl-terminated self-assembled monolayer(OH-SAM),as a surface resistant to protein adsorption,exhibits substantial potential in applications such as ship navigation and medical implants,and the appropriate strategies for designing anti-fouling surfaces are crucial.Here,we employ molecular dynamics simulations and alchemical free energy calculations to systematically analyze the factors influencing resistance to protein adsorption on the SAMs terminated with single or double OH groups at three packing densities(∑=2.0 nm^(-2),4.5 nm^(-2),and 6.5 nm^(-2)),respectively.For the first time,we observed that the compactness and order of interfacial water enhance its physical barrier effect,subsequently enhancing the resistance of SAM to protein adsorption.Notably,the spatial hindrance effect of SAM leads to the embedding of protein into SAM,resulting in a lack of resistance of SAM towards protein.Furthermore,the number of hydroxyl groups per unit area of double OH-terminated SAM at ∑=6.5 nm^(-2) is approximately 2 to 3 times that of single OH-terminated SAM at ∑=6.5 nm^(-2) and 4.5 nm^(-2),consequently yielding a weaker resistance of double OH-terminated SAM towards protein.Meanwhile,due to the structure of SAM itself,i.e.,the formation of a nearly perfect ice-like hydrogen bond structure,the SAM exhibits the weakest resistance towards protein.This study will complement and improve the mechanism of OH-SAM resistance to protein adsorption,especially the traditional barrier effect of interfacial water.
基金China Postdoctoral Science Foundation(2020M681125)National Natural Science Foundation of China(32272254,31901618)Collaborative Innovation Center of Fragrance Flavour and Cosmetics.
文摘Bio-based cyclodextrins(CDs)are a common research object in supramolecular chemistry.The special cavity structure of CDs can form supramolecular self-assemblies such as vesicles and microcrystals through weak interaction with guest molecules.The different forms of supramolecular self-assemblies can be transformed into each other under certain conditions.The regulation of supramolecular self-assembly is not only helpful to understand the self-assembly principle,but also beneficial to its application.In the present study,the self-assembly behavior of epoxy-β-cyclodextrin(EP-β-CD)and mixed anionic and cationic surfactant system(sodium dodecyl sulfate/dodecyltrimethylammonium bromide,SDS/DTAB)in aqueous solution was studied.Morphological and particle size characterization found that the SDS/DTAB@EP-β-CD complex,as the basic building unit,self-assembled into worm-like micelles at lower temperatures and vesicles at higher temperatures.Nuclear magnetic resonance(NMR)and Fourier transform infrared spectroscopy(FT-IR)analysis revealed that the driving force for the formation of vesicles and worm-like micelles was the hydrogen bonds between EP-β-CD molecules,while water molecules played an important role in promoting vesicle formation between SDS/DTAB@EP-β-CD units.Herein,the mechanism of the morphologic transformation of SDS/DTAB@EP-β-CD supramolecular aggregates induced by temperature was elucidated by exploring the self-assembly process,which may provide an excellent basis for the development of delivery carriers.
文摘In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.
基金Funded by the Key R&D Program of the Science and Technology Department of Hubei Province(No.2022BCE008)。
文摘Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface morphology and electric property was investigated.The results confirmed the dense membrane of PEDOT:PSS and the lamellar structure of PDDA-rGO on the fibers.It has excellent electrical conductivity and mechanical properties.The fiber based electrochemical transistor(FECTs)prepared by the composite conductive fiber has a maximum output current of 8.7 mA,a transconductance peak of 10 mS,an on time of 1.37 s,an off time of 1.6 s and excellent switching stability.Most importantly,the devices by layer by layer self-assembly technology opens a path for the true integration of organic electronics with traditional textile technologies and materials,laying the foundation for their later widespread application.
基金Supported by Heilongjiang Provincial Fruit Tree Modernization Agro-industrial Technology Collaborative Innovation and Promotion System Project(2019-13)。
文摘In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.
基金the National Key R&D Program of China(No.2023YFA1606503)the National Natural Science Foundation of China(Nos.12035011,11975167,11947211,11905103,11881240623,and 11961141003).
文摘Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics.Machine learning provides an innovative approach to exploring complex physical problems.In this study,the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process.First,the binding energies for 2238 nuclei with Z>20 and N>20 are calculated using the Gaussian process in a physically motivated feature space,yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV.The results show the good learning ability of the Gaussian process in the studies of binding energies.Then,the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020.The theoretical results are in good agreement with the experimental data,reflecting the good predictive power of the Gaussian process.Moreover,the-decay energies for 1169 nuclei with 50≤Z≤110 are derived from the theoretical binding energies calculated using the Gaussian process.The average deviation and the standard deviation are,respectively,0.047 MeV and 0.070 MeV.Noticeably,the calculated-decay energies for the two new isotopes 204 Ac(Huang et al.Phys Lett B 834,137484(2022))and 207 Th(Yang et al.Phys Rev C 105,L051302(2022))agree well with the latest experimental data.These results demonstrate that the Gaussian pro-cess is reliable for the calculations of nuclear binding energies.Finally,the-decay properties of some unknown actinide nuclei are predicted using the Gaussian process.The predicted results can be useful guides for future research on binding energies and-decay properties.
基金support of the National Key Research and Development Program of China(2021YFB4000505).
文摘Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LY18A010020)the Innovation of Jiaxing City:A Program to Support the Talented Persons.
文摘Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.